California
Assembly - 1018 - Automated decision systems.
Legislation ID: 25018
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of Assembly Bill 1018 (“Automated Decisions Safety Act”), organized as requested. Every claim is tied to specific bill text.
Section A: Definitions & Scope
1. “Artificial intelligence”
– “Artificial intelligence” means “an engineered or machine-based system…that can…infer from the input it receives how to generate outputs that can influence physical or virtual environments.”
• (Bill §22756(a), lines 4–8)
– Relevance: explicitly invokes AI as an umbrella for systems that learn or infer.
2. “Automated decision system” (ADS)
– “a computational process derived from machine learning, statistical modeling, data analytics, or artificial intelligence that issues simplified output…used to assist or replace human discretionary decisionmaking and materially impacts natural persons.”
• (§22756(b)(1), lines 9–14)
– Relevance: targets algorithmic models that make “scores,” “classifications,” or “recommendations.”
3. “Covered automated decision system” (covered ADS)
– “an automated decision system that makes or facilitates a consequential decision.”
• (§22756(d), lines 27–28)
4. “Consequential decision”
– Defined to include decisions in employment, education, housing, utilities, family planning, healthcare, financial services, criminal justice, elections, government benefits, places of public accommodation.
• (§22756(c), lines 18–25 through lines 33–38)
– Relevance: scope is very broad, capturing most high-stakes AI uses affecting civil rights.
5. “Developer” & “Deployer”
– “Developer” means those who “design, code, substantially modify, or otherwise produce an automated decision system…”
• (§22756(f), lines 34–37)
– “Deployer” means those who “use a covered ADS to make or facilitate a consequential decision…”
• (§22756(e), lines 30–33)
Section B: Development & Research
Although the bill does not directly mandate research funding or open-data sharing, it imposes pre-and post-deployment evaluation and audit requirements on developers.
1. Performance evaluations by developers
– Developers must conduct an initial “performance evaluation” before Jan 1 2027 (for ADS existing before Jan 1 2026) or “before initially deploying” any ADS first offered on or after Jan 1 2026.
• (§22756.1(a)(1)–(2), lines 3–11 and lines 19–26)
– Evaluations must assess:
• Purpose and “developer-approved uses” (§22756.1(b)(1)–(2), lines 36–38)
• Expected accuracy and reliability, including “reasonably foreseeable effects of fine tuning” (§22756.1(b)(3)(A)–(B), lines 4–6)
• Disparate treatment and disparate impact, whether “reasonably likely to occur,” necessity, alternatives, and mitigation (§22756.1(b)(4)–(5), lines 10–22 and lines 24–39)
Impact on R&D:
– Encourages rigorous bias and reliability testing early in development.
– May slow release of new models due to audit cycles, especially for startups with limited resources.
2. Third-party audits of developer compliance
– Developers must “contract with an independent third-party auditor” for compliance with §22756.1(b).
• (§22756.1(b)(6)(A), lines 7–8)
– Auditors get “any available information…reasonably necessary,” subject to trade-secret redactions.
• (§22756.1(b)(6)(B), lines 9–16)
Impact:
– Introduces a commercial market for AI compliance auditors.
– Protects trade secrets while giving regulators a check.
3. Documentation and record-keeping
– Developers must retain unredacted records for as long as the ADS is deployed plus 10 years.
• (§22756.1(g), lines 6–13)
Impact:
– Imposes long-term data-management burdens on developers.
– Enables retrospective analysis of system behavior.
Section C: Deployment & Compliance
1. Pre-deployment disclosures to subjects
– Before “finalizing a consequential decision,” deployers must give each subject a “plain language written disclosure” with:
• Notice that an ADS is used, name/version/developer, whether use is “developer-approved,” attributes measured, data sources, key parameters, output format, human review, rights to opt out/appeal, and contact info.
• (§22756.2(a)(1)(A)–(H), lines 30–38; lines 1–8)
– Exception: medical emergencies.
Impact:
– Highly increases transparency for end-users.
– May deter deployment in urgent or high-volume contexts.
2. Opt-out and appeal rights
– Subjects get “reasonable opportunity to opt out” (§22756.2(b)(1), lines 25–28)
– Exceptions: certain financial decisions under Gramm-Leach-Bliley Act or medical emergencies (§22756.2(b)(2), lines 29–35)
– Post-decision right to correct data or appeal outcome within 30 business days, with deployers required to review and, if erroneous, “rectify the decision” (§22756.2(d)(1)–(2), lines 1–11 and lines 22–31)
Impact:
– Empowers individuals to challenge AI-driven decisions.
– Increases operational overhead for deployers (customer service, legal).
3. Audits of deployed systems
– Deployers impacting >5,000 people in 3 years must “contract with an independent third-party auditor” for an impact assessment before Jan 1 2030 and every 3 years thereafter.
• (§22756.2(g)(1), lines 16–20)
– Audits cover observed accuracy/reliability, disparities vs. developer expectations, unanticipated impacts, out-of-scope uses, and assumption of developer duties.
• (§22756.3(a)(1)–(5), lines 14–32)
Impact:
– Encourages continuous monitoring of real-world performance.
– May impose heavy costs on large deployers (e.g., universities, hospitals, credit-issuers).
4. Trade-secret protections
– Both developers and deployers may redact trade-secret information when sharing with auditors or subjects, provided they notify and justify.
• (§22756.1(b)(6)(B)(ii), lines 14–17; §22756.2(e)(1), lines 1–4)
Impact:
– Balances transparency with IP protection.
– Leaves open interpretation of “reasonable” redactions and may breed disputes.
Section D: Enforcement & Penalties
1. AG access & PR Act exemption
– Within 30 days of an AG request, any developer, deployer, or auditor must provide unredacted evaluations/assessments.
• (§22756.4(a), lines 17–21)
– These records are exempt from the California Public Records Act.
• (§22756.4(b)(2), lines 30–33)
Impact:
– Ensures regulator access while preserving confidentiality from the public.
2. Authorized enforcers & remedies
– Civil actions may be brought by the Attorney General, district attorneys, city attorneys, Civil Rights Department, and (for employment decisions) the Labor Commissioner.
• (§22756.5(a), lines 38–40; lines 1–8)
– Remedies include injunctive and declaratory relief, attorney fees, and civil penalties up to $25,000 per violation.
• (§22756.5(b), lines 9–16)
Impact:
– Strong enforcement powers create real risk for noncompliance.
– Multiple enforcement channels increase the chance of scrutiny.
3. Liability for vendors
– A developer or deployer remains liable for third-party contractors’ failures.
• (§22756.8, lines 36–39)
Impact:
– Forces careful vendor management and contract terms.
Section E: Overall Implications
1. Transparency & Accountability
– The bill mandates extensive documentation, disclosure, and audit trails throughout the AI lifecycle. This could raise public trust but also create significant compliance costs.
2. Innovation vs. Compliance Burden
– Startups and academic labs may be slowed by required performance evaluations and mandatory third-party audits, especially for systems addressing any “consequential decisions.” Established vendors will need to spin up compliance organizations.
3. Market for Auditors & Consultants
– New opportunities for specialized AI auditors, compliance officers, and legal advisors.
4. Civil-rights alignment
– Integrates AI oversight into existing civil-rights and consumer-protection frameworks (e.g., Unruh Act amendments at Civil Code §51).
5. Ambiguities & Risks
– Terms like “reasonably foreseeable,” “reasonable opportunity,” and “materially impacts” are open to interpretation. Disputes over scope (e.g., whether a given ADS “materially impacts” a person) may require litigation or AG guidance.
In sum, AB 1018 would erect one of the most comprehensive state-level AI regulatory regimes in the nation—emphasizing risk assessment, individual rights, and civil-rights enforcement, while balancing proprietary interests via trade-secret carve-outs.
Assembly - 1064 - Leading Ethical AI Development (LEAD) for Kids Act.
Legislation ID: 25064
Bill URL: View Bill
Sponsors
Assembly - 1137 - Artificial intelligence: data transparency.
Legislation ID: 25135
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an analysis of Assembly Bill 1137 (“AB 1137”), as introduced February 20, 2025. Because this draft bill consists solely of amendments to the Civil Code’s definition section (Section 3110) and contains no substantive provisions on funding, deployment requirements, penalties, or enforcement beyond definitions, most of the standard headings (Sections B–D) remain empty or “Not Applicable.” Section A identifies and explains each AI-related definition. Section E provides overall implications given the bill’s narrow scope.
SECTION A: DEFINITIONS & SCOPE
AB 1137 revises Section 3110 of the Civil Code, which provides the cast of defined terms that determine the scope of California’s data-transparency requirements for “generative artificial intelligence” systems. All substantive AI-related provisions in the bill appear as defined terms under Section 3110(a)–(f).
1. “Artificial intelligence” (Section 3110(a))
• Text (lines 3–7):
“(a) ‘Artificial intelligence’ means an engineered or machine-based system that varies in its level of autonomy and that can, for explicit or implicit objectives, infer from the input it receives how to generate outputs that can influence physical or virtual environments.”
• Analysis: This broad definition captures any system—software or hardware—that processes inputs and produces outputs aimed at influencing environments. By not limiting it to “learning” or “neural” methods, the definition can encompass rule-based, evolutionary, or statistical AI.
• Relevance: Establishes the umbrella term under which all subsequent definitions (e.g., “generative artificial intelligence”) fall.
2. “Developer” (Section 3110(b))
• Text (lines 3–9):
“(b) ‘Developer’ means a person, partnership, state or local government agency, or corporation that designs, codes, produces, or substantially modifies an artificial intelligence system or service for use by members of the public. For purposes of this subdivision, ‘members of the public’ does not include an affiliate as defined in subparagraph (A) of paragraph (1) of subdivision (c) of Section 1799.1a, or a hospital’s medical staff member.”
• Analysis: The term “developer” is key because California’s data-transparency law applies to developers of generative AI systems. Excluding “affiliates” and “medical staff” narrows the law’s reach, potentially allowing internal or clinically-licensed uses to escape the transparency regime.
3. “Generative artificial intelligence” (Section 3110(c))
• Text (lines 10–14):
“(c) ‘Generative artificial intelligence’ means artificial intelligence that can generate derived synthetic content, such as text, images, video, and audio, that emulates the structure and characteristics of the artificial intelligence’s training data.”
• Analysis: Explicitly targets models like large language models, image-synthesis networks, deepfakes, etc. This definition triggers the bill’s upstream transparency obligations (e.g., posting documentation about training data).
4. “Substantially modifies” / “substantial modification” (Section 3110(d))
• Text (lines 14–18):
“(d) ‘Substantially modifies’ or ‘substantial modification’ means a new version, new release, or other update to a generative artificial intelligence system or service that materially changes its functionality or performance, including the results of retraining or fine tuning.”
• Analysis: Defines the threshold for when a developer must renew or update its transparency disclosures. By including retraining and fine-tuning, it ensures that even iterative updates re-trigger disclosure requirements.
5. “Synthetic data generation” (Section 3110(e))
• Text (lines 19–21):
“(e) ‘Synthetic data generation’ means a process in which seed data are used to create artificial data that have some of the statistical characteristics of the seed data.”
• Analysis: Captures data-augmentation workflows often used to bolster training sets. This term will matter for transparency around what data sources have been synthesized rather than directly collected.
6. “Train a generative artificial intelligence system or service” (Section 3110(f))
• Text (lines 22–24):
“(f) ‘Train a generative artificial intelligence system or service’ includes testing, validating, or fine tuning by the developer of the artificial intelligence system or service.”
• Analysis: Broadens “training” beyond initial parameter fitting to include downstream tasks. This helps ensure that all phases of model development, including evaluation and hyperparameter searches, are covered by the transparency regime.
SECTION B: DEVELOPMENT & RESEARCH
Not Applicable. AB 1137 contains no provisions on research funding, grants, university partnerships, or public–private data-sharing mandates beyond the definitions.
SECTION C: DEPLOYMENT & COMPLIANCE
Not Applicable. This bill does not set certification processes, auditing standards, liability rules, or compliance mechanisms for deployed AI systems. AB 1137 only amends definitions that underpin other transparency requirements found elsewhere in the Civil Code.
SECTION D: ENFORCEMENT & PENALTIES
Not Applicable. No new enforcement mechanisms, penalties, or incentives are attached to the definitions in Section 3110.
SECTION E: OVERALL IMPLICATIONS
1. Narrow, Non-Substantive Change
• AB 1137 performs only a nonsubstantive cleanup of existing definitions. Its legislative summary confirms this by stating it “would make a nonsubstantive change to those definition provisions.”
2. Foundational Role for Transparency Law
• Though this bill itself does not impose new obligations, its amended definitions determine the scope of California’s existing requirement (in Civil Code Section 3120 et seq.) that generative AI developers publish documentation about their training data before offering services to Californians.
3. Impact on Developers
• By clarifying terms like “substantial modification” and “synthetic data generation,” developers must monitor even incremental updates (e.g., fine-tuning) and data-synthesis steps to remain compliant. Ambiguities remain around what “materially changes” functionality means in practice—a point developers and regulators may litigate or clarify later.
4. No Direct Effect on Researchers or Commercial Deployment
• Because AB 1137 does not touch funding, liability, or user-facing disclosures beyond definitions, it does not directly restrict or advance research agendas, nor does it impose new requirements on commercially deployed AI systems.
In summary, AB 1137’s sole effect is to refine the definitions that underpin California’s existing generative AI data-transparency regime. It does not, by itself, create any new substantive rights, obligations, enforcement tools, or incentives beyond clarifying who and what counts as a “developer,” what constitutes “generative artificial intelligence,” and when an AI system is “substantially modified.”
Assembly - 1405 - Artificial intelligence: auditors: enrollment.
Legislation ID: 25389
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. “Artificial intelligence” (Section 11549.80(b))
• Text: “Artificial intelligence or AI means an engineered or machine-based system that varies in its level of autonomy and that can, for explicit or implicit objectives, infer from the input it receives how to generate outputs that can influence physical or virtual environments.”
• Analysis: This broad definition encompasses any ML, statistical, or rule-based system capable of autonomous inference. It explicitly targets AI systems by focusing on autonomy and inference, not just predetermined rules.
2. “Automated decision system” (Leg. Counsel’s Digest)
• Text: “Existing law defines ‘automated decision system’ as a computational process … that issues simplified output … used to assist or replace human discretionary decisionmaking and materially impacts natural persons.”
• Analysis: Though not re-stated in the body, the bill’s context sits atop existing law regulating high-risk automated decision systems, reinforcing the scope to AI-driven decisioning.
3. “Artificial intelligence auditor” (Section 11549.80(c))
• Text: “AI auditor means a person, partnership, or corporation that assesses an AI system or model on behalf of a third party.”
• Analysis: Creates a regulated class of professionals whose sole purpose is to audit AI. Their definition ties directly to “AI system or model,” thus excluding non-AI technologies.
4. “Covered audit” (Section 11549.80(d))
• Text: “Covered audit means an audit conducted pursuant to any state statute that requires an audit of an AI system or model by an independent third party auditor.”
• Analysis: Limits the bill’s audit requirements to those mandated by other statutes—likely those governing high-risk automated decision systems.
Section B: Development & Research
No direct funding, R&D mandates, or data-sharing rules appear. The bill’s focus is on auditing and transparency, not on research support. However:
• Ambiguity: The bill requires auditors to disclose “types of AI systems or models that the auditor is qualified to audit” (11549.83(a)(3)(C)), which could indirectly shape research priorities if auditors publicly list areas of expertise.
Section C: Deployment & Compliance
1. Enrollment Requirement (11549.83(a))
• Text: “Beginning January 1, 2027, prior to initially conducting a covered audit, an AI auditor shall … (1) Enroll with the agency … (2) Pay … an enrollment fee … (3) Provide … name, contact info, types of AI systems or models … qualifications … SOP.”
• Impact: Creates a gatekeeping regime for AI auditors. Startups or small consultancies may face new compliance costs (fees, SOP documentation).
2. Transparency & Public Listing (11549.82(b)(1))
• Text: “Beginning January 1, 2027 … publish any information provided by an enrolled AI auditor … in a publicly accessible format.”
• Impact: Potentially raises the profile of auditing firms, enabling end-users to shop based on auditor qualifications. Could spur market competition among auditors.
3. Audit Standards (11549.82(a)(2), 11549.84(a),(b))
• Text: “Fix enrollment fees at an amount not exceeding the reasonable costs of administering this chapter.” (Fees)
“In conducting a covered audit, an enrolled AI auditor shall abide by generally accepted industry best practices.” (11549.83(b))
“After conducting a covered audit … provide … an audit report that contains … results … steps to meet industry standards … steps … to become compliant with state law.” (11549.84(a))
• Impact: By mandating “generally accepted industry best practices” and standardized reporting, the bill drives harmonization of auditing output. Vendors must align models to common benchmarks to pass audits.
4. Cooling-off Period & Conflict Prohibitions (11549.84(d),(e))
• Text: “An enrolled AI auditor shall not conduct a covered audit if it has a financial interest in the auditee.” (d)
“Shall not accept employment with an auditee within 12 months of completing a covered audit.” (e)
• Impact: Prevents “audit shopping” or conflicts of interest, bolstering audit independence. May discourage small firms from offering both auditing and consulting, narrowing service offerings.
Section D: Enforcement & Penalties
1. Confidentiality & Whistleblower Protections (11549.85)
• Permitted Disclosures: subpoenas; defense in legal proceedings; regulatory inquiry; due diligence in sale/merger; professional peer review. (11549.85(a)(1)–(6))
• Prohibitions: “Shall not prevent an employee from disclosing information to the Attorney General or the Labor Commissioner, or using … the misconduct reporting mechanism…; shall not retaliate against an employee” (11549.85(b)(1)–(2)).
• Impact: Provides legal safe harbor for internal reporting of audit misconduct. Encourages enforcement via whistleblowers rather than licensing board actions.
2. Record-Retention & Public Reporting (11549.82(b)(2), 11549.84(c))
• Text: “Retain any report … for as long as the auditor remains enrolled, plus 10 years.”
“An enrolled AI auditor shall retain any documentation … for at least 10 years.”
• Impact: Establishes long-term audit trail, enabling regulators or aggrieved parties to investigate historic audits. Non-compliance could expose auditors to enforcement but bill lacks explicit fines or license revocation language.
Section E: Overall Implications
• Market Formation for AI Auditors: By licensing and publicizing auditor credentials, the state creates a new professional category with entry costs (fees, SOP documentation) and compliance burdens.
• Standardization & Consumer Confidence: Mandatory adherence to “industry best practices” and standardized audit reports can raise the baseline quality and comparability of AI audits, benefitting end-users and regulators.
• Limited Scope on Research Funding: The bill does not address AI R&D incentives or data-sharing, focusing narrowly on auditing.
• Enforcement via Whistleblowers & Transparency: Rather than specifying fines or revocation, the bill relies on public reporting mechanisms and whistleblower protection to police auditor misconduct.
• Potential Chilling on Small Auditors: The fixed costs of enrollment and documentation (SOP, long-term retention) may inhibit small or solo practitioners, concentrating the market among larger firms.
• Ambiguities: “Generally accepted industry best practices” is undefined—auditors and auditees may contest what practices qualify. The bill defers fee-setting to the agency, making budgeting unpredictable until finalized.
Assembly - 279 - School libraries: model library standards.
Legislation ID: 24316
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is my analysis of AB 279 with an AI-focus, organized per your requested structure. In short, the bill is not an AI-centric regulatory framework; it merely mandates revisiting school library standards and includes AI only as one among several emerging technologies to be considered by an expert panel.
Section A: Definitions & Scope
—The bill contains no standalone definitions of “artificial intelligence,” “AI system,” or related terms.
—It does not expand or modify the scope of existing Education Code definitions.
—The only reference to AI is embedded as one topic area for expert consideration (see below). Because there is no definitional section, the bill neither constrains nor clarifies what “artificial intelligence” means in this context, leaving it open to interpretation by the convened expert group and the Instructional Quality Commission.
Section B: Development & Research
—No provisions impose funding mandates, reporting requirements, data-sharing rules, or R&D obligations specifically targeted at AI.
—The one research‐adjacent element is the requirement that, on or before July 1, 2028 (and every eight years thereafter), the Superintendent of Public Instruction “consider convening a group of experts in the fields of literacy, technology and media to recommend revisions to the standards for school library services” (Section 60605.14(a), lines 3–7).
• Among those experts, at least half must be credentialed teacher librarians, and the panel must include “[t]eachers who work regularly with trending technologies, media literacy, artificial intelligence, and social media in grades 1 to 12, inclusive” (subdivision (a)(1)(A), lines 11–14).
Implication: This language implicitly acknowledges AI as part of the broader “trending technologies” ecosystem that K–12 teachers may engage with, but it places AI alongside social media and media literacy rather than singling it out for special R&D treatment.
Section C: Deployment & Compliance
—The bill does not create any compliance regime for AI systems, no certification process, auditing requirements, or liability rules.
—It merely tasks the Instructional Quality Commission with “consider[ing] developing and recommending revisions to the standards for school library services, based on the recommendations made pursuant to subdivision (a), to the state board” (Section 60605.14(b), lines 24–27).
• These “standards for school library services” are those initially adopted under Education Code section 18101. There is no text in AB 279 that would add new mandatory controls on how AI is deployed in libraries or schools.
Section D: Enforcement & Penalties
—There are no enforcement mechanisms, penalties, or incentives tied specifically to AI.
—The only conditionality is fiscal: “The operation of this section is subject to an appropriation being made for purposes of this section in the annual Budget Act or another statute” (Section 60605.14(c), lines 28–30).
• If no funds are appropriated, none of the convening or standards-revision activities would take place.
Section E: Overall Implications for the State’s AI Ecosystem
1. Minimal Regulatory Impact: The bill does not regulate AI systems, products, or developers.
2. Recognition—Not Regulation: By listing “artificial intelligence” among “trending technologies,” the bill signals that AI should be part of future school library standards, but it leaves all substantive policy development to future expert panels and the Instructional Quality Commission.
3. Open Definitions: Lack of AI definitions or scope means that panels will have to decide what “artificial intelligence” entails in the context of K–12 library services—potentially leading to wide variance in interpretation.
4. No Direct Research or Deployment Drivers: Startups, researchers, and vendors will find no new mandates or incentives here; the bill does not create grant programs, data-sharing obligations, or compliance hurdles for AI.
5. Indirect Educational Influence: Over time, library standards may come to include recommended AI literacy curricula or recommended AI tools for libraries—as determined by the future expert group and the Commission—but those would be advisory until (and unless) the State Board of Education turns them into binding regulations.
In sum, AB 279 mentions AI only as one facet of a broader “technology and media” landscape to be examined by an expert group for potential updates to school library standards. It neither defines AI nor regulates AI development, deployment, or enforcement.
Assembly - 316 - Artificial intelligence: defenses.
Legislation ID: 24353
Bill URL: View Bill
Sponsors
Assembly - 410 - Bots: disclosure.
Legislation ID: 24445
Bill URL: View Bill
Sponsors
Assembly - 412 - Generative artificial intelligence: training data: copyrighted materials.
Legislation ID: 24447
Bill URL: View Bill
Sponsors
Assembly - 489 - Health care professions: deceptive terms or letters: artificial intelligence.
Legislation ID: 24518
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. “Artificial intelligence” (AI)
- “Artificial intelligence has the same meaning as set forth in Section 11546.45.5 of the Government Code.” (Bill, § 4999.8(a), lines 8–10)
• Relevance: This imports California’s existing, statutory definition of AI—likely covering “machine learning, deep learning, or other algorithmic or statistical methods”—into the healing arts context.
• Ambiguity: We must check Government Code § 11546.45.5 for whether it covers only generative models or all automated decision-making systems. Depending on that scope, the bill could apply narrowly to generative chatbots or broadly to any software with “AI.”
2. “Health care profession”
- Defined as “any profession that is the subject of licensure or regulation under [Division 2 of the B&P Code].” (Bill, § 4999.8(b), lines 1–4)
• Relevance: Ensures that the AI restrictions apply to AI tools claiming to practice medicine, dentistry, nursing, pharmacy, etc.
Section B: Development & Research
– No clauses in AB 489 impose research-specific mandates (e.g., reporting requirements, data-sharing).
– The bill does not regulate AI model training, data collection, or academic research in health AI.
Section C: Deployment & Compliance
1. Extension of existing “unauthorized practice” rules to AI
- “Any provision of this division that prohibits the use of specified terms, letters, or phrases … shall be enforceable against a person or entity who develops or deploys … [AI] that uses one or more of those terms … in the advertising or functionality” (Bill, § 4999.9(b), lines 11–15).
• Relevance: Binds AI vendors—not just unlicensed humans—to laws against implying licensure.
• Impact on vendors: They must audit UI text, marketing copy, and system prompts to avoid terms like “doctor,” “MD,” “DDS” unless a real licensed professional supervises.
2. Prohibited implication of a licensed provider
- “The use of a term, letter, or phrase … that indicates or implies that the care or advice being offered … is being provided by a natural person in possession of the appropriate license … is prohibited.” (Bill, § 4999.9(c), lines 16–21)
• Relevance: Targets “AI chatbots” that might say, “I’m Dr. Smith and I recommend…”
• Impact on end-users: Clearer disclosures required; end-users know advice comes from AI.
• Impact on startups: Additional UI/UX and legal compliance costs to label AI appropriately.
3. Separate violations
- “Each use of a prohibited term, letter, or phrase shall constitute a separate violation.” (Bill, § 4999.9(d), lines 22–23)
• Relevance: Creates strict liability per incident—advertising one prohibited title across multiple pages or sessions multiplies penalties.
• Impact on AI deployment: Heightens compliance burden; vendors must ensure templates, logs, and content dynamically avoid forbidden language.
Section D: Enforcement & Penalties
1. Jurisdiction
- “A violation of this chapter is subject to the jurisdiction of the appropriate health care professional licensing board or enforcement agency.” (Bill, § 4999.9(a), lines 4–7)
• Relevance: Enforcement falls to boards like the Medical Board of California, already tasked with policing unauthorized practice.
• Impact on regulators: Boards must expand oversight to include AI developers—potentially requiring new investigatory processes.
2. State-mandated local program
- The bill “imposes a state-mandated local program” by expanding existing crimes to cover AI entities. (Legislative summary, lines 24–29)
• Impact on local agencies: May increase workload for District Attorneys or local regulators handling infractions.
Section E: Overall Implications
1. Restrictive clarity on “practice of medicine”
- By extending unauthorized-practice rules to AI, AB 489 discourages AI systems from simulating licensed professionals without human oversight or disclaimers.
- Likely result: AI health advice tools must prominently brand themselves as “AI” or “computerized,” not “doctor,” reducing patient confusion but also raising development costs.
2. Limited R&D impact
- No mandates on data sharing, safety testing, or model auditing—contrast to broader AI bills that impose governance structures.
- R&D in academic and non-commercial settings remains largely unaffected.
3. Focus on consumer protection
- The bill’s main thrust is consumer transparency: patients must not mistake AI for a licensed individual.
- Vendors and startups will need compliance teams to vet UI text, marketing materials, and dynamic responses for disallowed terms.
4. Potential chilling effect
- Strict per-use violations may deter small developers from entering the health AI space.
- Established vendors, better capitalized, can absorb compliance costs and may consolidate market share.
5. Regulatory coordination needed
- Health boards must develop expertise in AI systems.
- Possible emergence of certification standards or “safe harbor” labeling guidelines for “AI health advice.”
Assembly - 853 - California AI Transparency Act.
Legislation ID: 24863
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an analysis of AB 853 (“California AI Transparency Act”) in the structure you requested. All quotations are drawn from the text you supplied.
Section A: Definitions & Scope
1. “Covered provider” and “GenAI system” (implicitly AI)
• The bill repeatedly uses the terms “covered provider” and “GenAI system,” which it does not explicitly define in Section 22757.2. By context, a “covered provider” is any person who “creates, codes, or otherwise produces a generative artificial intelligence system that has over 1,000,000 monthly visitors or users and is publicly accessible within the geographic boundaries of the state.” (Digest, lines 1–5.)
• “GenAI system” is shorthand for “generative artificial intelligence system.” That term appears in subdivision (a)(1)–(4), (b), and (c). For example:
– Subdivision (a)(1): “whether … content … was created or altered by the covered provider’s GenAI system.”
– Subdivision (a)(4)(B): “prevent, or respond to, demonstrable risks to the security or integrity of its GenAI system.”
2. “AI detection tool”
• The requirement centers on an “AI detection tool.” The term is introduced in subdivision (a): “A covered provider shall make available an AI detection tool at no cost to the user…” (a, line 1).
• The tool must permit users to assess whether content was “created or altered by that person’s generative artificial intelligence system.” (Digest, lines 4–8.)
Section B: Development & Research
– No explicit funding mandates, R&D reporting, or data-sharing requirements appear in the text of § 22757.2. The bill focuses narrowly on operational transparency via an AI detection tool, not on R&D processes.
Section C: Deployment & Compliance
1. Mandatory Tool Functionality (subdivision a)
• (a)(1): “The tool allows a user to assess whether image, video, or audio content, or content that is any a combination thereof, was created or altered by the covered provider’s GenAI system.”
• (a)(2): “The tool outputs any system provenance data that is detected in the content.”
• (a)(3): “The tool does not output any personal provenance data that is detected in the content.”
• (a)(5): “The tool allows a user to upload content or provide a uniform resource locator (URL) linking to online content.”
• (a)(6): “The tool supports an application programming interface that allows a user to invoke the tool without visiting the covered provider’s internet website.”
2. Accessibility vs. Security Limits
• (a)(4)(A): “Subject to subparagraph (B), the tool is publicly accessible.”
• (a)(4)(B): “A covered provider may impose reasonable limitations on access to the tool to prevent, or respond to, demonstrable risks to the security or integrity of its GenAI system.”
– Implication: Providers must strike a balance between open access and safeguarding their own models from abuse.
3. User Feedback Loop (subdivision b)
• “A covered provider shall collect user feedback related to the efficacy of the covered provider’s AI detection tool and incorporate relevant feedback into any attempt to improve the efficacy of the tool.”
– This creates an ongoing compliance obligation and continuous improvement process.
4. Data-minimization & Privacy (subdivision c)
• (c)(1)(A): “Except as provided in subparagraph (B), collect or retain personal information from users of the … AI detection tool.”
• (c)(1)(B)(i–ii): Providers may only collect contact info from users who “opt in” when submitting feedback, and then only to “evaluate and improve the efficacy of the … tool.”
• (c)(2): “Retain any content submitted to the AI detection tool for longer than is necessary to comply with this section.”
• (c)(3): “Retain any personal provenance data from content submitted to the AI detection tool by a user.”
– Implication: Strict limits on data retention and personal data use, reducing privacy risks to end users.
Section D: Enforcement & Penalties
– The text of § 22757.2 contains no express penalties, fines, or enforcement procedures. It simply states duties (“shall make available,” “shall collect,” “shall not do”). Enforcement mechanisms—civil penalties, private right of action, state enforcement—would need to come from other statutes or accompanying legislative language not shown here.
Section E: Overall Implications
1. Transparency & Trust
• By mandating free, public access to a detection tool and disclosure of “system provenance data” (a)(2), the bill aims to increase public trust in media authenticity and in AI systems.
2. Minimal Impact on R&D
• No R&D mandates or reporting requirements: startups and researchers remain free to innovate, but must build detection capabilities once they cross the 1 million monthly user threshold.
3. Operational Costs for Providers
• Covered providers (those with large public usage) will need to invest in:
– Infrastructure for hosting a public tool and API.
– Ongoing feedback collection and tool refinement.
– Data privacy compliance to purge personal provenance data.
4. Competitive Dynamics
• Incumbent large AI vendors will absorb these costs but gain a transparency advantage. Smaller players below the user threshold escape the requirement, potentially creating a user-base threshold to avoid compliance.
5. Regulatory Precedent
• Establishes California as a forerunner in mandating AI provenance tools, possibly influencing other jurisdictions or prompting federal action.
Ambiguities & Notes
– “System provenance data” vs. “personal provenance data” are not defined in detail; providers must interpret these terms, which may lead to inconsistency in reporting.
– No timeline or effective date is provided in § 22757.2, leaving unclear when compliance must begin.
In sum, AB 853 narrowly targets large-scale generative AI deployments in California by requiring free detection tools with tight privacy safeguards. It emphasizes transparency and user feedback without imposing broad R&D or punitive measures.
Assembly - 979 - Artificial intelligence.
Legislation ID: 24983
Bill URL: View Bill
Sponsors
Senate - 11 - Artificial intelligence technology.
Legislation ID: 25563
Bill URL: View Bill
Sponsors
Senate - 238 - Employment: artificial intelligence.
Legislation ID: 25659
Bill URL: View Bill
Sponsors
Senate - 243 - Chatbots: minors.
Legislation ID: 25663
Bill URL: View Bill
Sponsors
Senate - 259 - Affinity-based algorithmic pricing.
Legislation ID: 25673
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of the single‐section SB 259 draft you provided. Because the bill is currently only an “intent” statement (no operative rules yet), many standard headings (R&D funding, certification regimes, etc.) have no content. I have nonetheless followed your requested structure and noted where the text is silent or ambiguous.
Section A: Definitions & Scope
1. “Affinity-based algorithmic pricing”
• Text: “affinity-based algorithmic pricing, which combines personalized pricing and dynamic pricing to determine differential pricing for a targeted group of consumers based on explicit or perceived characteristics gathered from personal data” (lines 2–7).
• Analysis: This is the only definition in the bill. It explicitly implicates AI-style systems (algorithms processing personal data to set prices). Because it mentions both “personalized pricing” and “dynamic pricing,” it covers machine-learning-driven price optimization engines that segment customers by demographics, browsing history, or inferred traits.
2. Absence of broader AI definitions
• The bill does not define “algorithm” or “personalized pricing” in technical terms, nor does it specify whether it includes rule-based systems versus learning-based models. This ambiguity could lead to debate over whether simple rule engines (e.g., “loyalty discount” codes) are captured.
Section B: Development & Research
• The text contains no provisions on R&D funding, university partnerships, data-sharing mandates for AI research, or technology development grants.
• Because SB 259 is purely an intent declaration (Section 1: “It is the intent of the Legislature to enact legislation that would ban…”), it does not require any reports, research disclosures, or collaborative studies.
Section C: Deployment & Compliance
1. Implicit prohibition on certain AI pricing models
• Although the bill does not yet include an operative ban, its stated intent (§ 1) announces an upcoming prohibition on “affinity-based algorithmic pricing.” In practice, once fleshed out, businesses deploying any AI-driven dynamic or personalized pricing system will need to evaluate whether their models classify as “affinity-based.”
2. Uncertainty on compliance scope
• Because there is no operative text, we lack clarity on:
– Which entities (online platforms, retailers, insurers?) will be subject to the ban.
– What level of data processing or inference (“explicit or perceived characteristics”) triggers violation.
– Whether any exemptions apply (small businesses, opt-in consumer consent, etc.).
Section D: Enforcement & Penalties
• The draft states only the Legislature’s intent; there are no enforcement mechanisms, penalty structures, private right of action, or civil fines described.
• Absent any fiscal or appropriations committee referrals, the bill as drafted imposes no immediate state-mandated programs or budgets for enforcement.
Section E: Overall Implications
1. Toward greater transparency and nondiscrimination
• By targeting “opaque and discriminatory pricing structures” (lines 6–7), the Legislature is signaling concern over hidden AI pricing biases. This could advance consumer protection against hidden algorithmic discrimination.
2. Restrictive impact on AI vendors and retailers
• A future ban could force startups and incumbents to redesign pricing engines to exclude any demographic inference or group-based segmentation. Companies may need to revert to simple, uniform pricing or self-report all personalization methods for regulatory review.
3. Ambiguity risks chilling innovation
• The lack of precise definitions and carve-outs may discourage legitimate uses of dynamic pricing (e.g., real-time surge pricing in rideshare) for fear of triggering the ban.
4. Regulatory pathway
• With no fiscal committee involvement or operative text, the next step will likely be earnest drafting of definitions, scope, enforcement, and exceptions. Stakeholders should monitor amendment hearings and prepare to propose clarifications around technical specifics (e.g., supervised vs. unsupervised models, allowable features).
Because SB 259 currently consists solely of an intent statement, the substantive AI rules will come only if and when the Legislature follows through with a full ban draft. At that point, each of the categories above (definitions, R&D, deployment standards, enforcement) will need concrete language.
Senate - 366 - Employment.
Legislation ID: 25751
Bill URL: View Bill
Sponsors
Senate - 420 - Individual rights.
Legislation ID: 25791
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused breakdown of SB 420, organized into the sections you requested. Every observation is tied back to the bill’s text via exact citations.
Section A: Definitions & Scope
1. No standalone “Definitions” section appears in the body of the proposed statutory text. However, the Legislative Counsel’s Digest (LCD) at lines 1–5 invokes AI-specific terminology:
– “covered provider … of a generative artificial intelligence system” (LCD, lines 1–2)
– “AI detection tool … outputs any system provenance data … that is detected in the content” (LCD, lines 2–5)
These LCD references signal that the draft contemplates future rules on providers of generative AI and on tools that detect AI-generated content.
2. Scope statements in § 1 confirm that this entire act is about “artificial intelligence technologies” (line 3) and “artificial intelligence systems” (lines 7, 9, etc.).
Section B: Development & Research
No provisions in SB 420 expressly target funding mandates, research reporting, academic-industry data sharing, or public grants. The text is entirely rights- and deployment-oriented.
– Absence of clauses such as “The state shall fund…” or “Entities shall report research findings…” indicates no direct effect on R&D pipelines.
Section C: Deployment & Compliance
SB 420 lays out a suite of requirements on any entity deploying or using AI systems that “impact California residents.”
1. Explanation Rights
– “Individuals should have the right to receive a clear and accessible explanation about how artificial intelligence systems operate…” (§ 1(b)(1), lines 7–9)
– Any “entity that uses artificial intelligence systems to make decisions impacting California residents should provide a mechanism to inform individuals of the system’s logic…” (§ 1(b)(2), lines 10–14)
Impact: Startups and incumbents will need to build explainability interfaces or documentation to satisfy this.
2. Data Privacy & Consent
– “All individuals have the right to control their personal data in relation to artificial intelligence systems” (§ 1(c)(1), lines 15–19)
– “Entities should obtain informed, explicit consent from individuals, and individuals should have the right to withdraw consent at any time without penalty” (§ 1(c)(2), lines 20–23)
– “Entities should ensure that personal data … is anonymized or pseudonymized if feasible” (§ 1(c)(3), lines 24–27)
Impact: Any AI vendor must implement consent-management systems, anonymization pipelines, and possibly data subject access workflows.
3. Non-discrimination & Bias Auditing
– “Artificial intelligence systems should not discriminate … based on race, gender, sexual orientation, disability, religion, socioeconomic status…” (§ 1(d)(1), lines 28–31)
– “Entities deploying artificial intelligence technologies should perform regular audits to identify and address any biases…” (§ 1(d)(2), lines 32–36)
Impact: Organizations will face ongoing algorithmic bias assessments and will need to audit datasets, models, and outcomes for protected-class fairness.
4. Accountability & Redress
– “Individuals should have the right to hold entities accountable for any harm … and entities should be liable for the actions and decisions made by artificial intelligence technologies they deploy” (§ 1(e)(1), lines 37–40)
– “An individual … should have access to a straightforward and transparent process for seeking redress, including the ability to challenge those decisions through human review and appeal mechanisms” (§ 1(e)(2), lines 1–5)
Impact: Vendors may face expanded liability, plus obligations to create appeal workflows and human-in-the-loop processes.
5. Human Oversight in High-Stakes Contexts
– “Individuals should have the right to request human oversight for significant decisions … particularly in areas such as employment, health care, housing, education, and criminal justice” (§ 1(f)(1), lines 6–9)
– “Artificial intelligence systems in high-stakes decisionmaking contexts should involve human review or intervention before final decisions” (§ 1(f)(2), lines 10–13)
Impact: Any AI application in regulated sectors must incorporate a human-in-the-loop checkpoint prior to action.
Section D: Enforcement & Penalties
– SB 420 expresses rights and duties but omits explicit enforcement mechanisms, private rights of action, or civil penalty schedules.
– No text allocates authority to a state agency, nor does it specify fines, injunctions, or corrective-action orders.
– This lack of enforcement language creates ambiguity: either enforcement will be spelled out in subsequent legislation (per § 2, lines 14–16) or regulators will need new rulemaking power.
Section E: Overall Implications
1. Advance or Restrict?
– By establishing clear rights around explanation, consent, bias auditing, and human review, SB 420 erects compliance bars that may slow rapid deployment of AI. Startups and researchers will need to build or buy tools for explainability, bias testing, data management, and appeals.
– Conversely, the bill could encourage innovation in “AI governance” services—e.g., consent-management platforms, bias-audit firms, and redress-process software.
2. Who’s Affected?
– Researchers: Unlikely to affect academic labs immediately, since the measure focuses on “entities deploying … impacting California residents.”
– Startups & Vendors: Must budget for new compliance workflows and potential legal liability.
– End-Users: Gain stronger rights to understand, control, and contest AI decisions.
– Regulators: Will need to draft implementing regulations, define “harm,” and possibly establish a new enforcement office or expand existing bodies (e.g., the California Privacy Protection Agency).
3. Ambiguities & Next Steps
– “Liable for the actions and decisions” (§ 1(e)(1)) could be read as strict liability or negligence-based; draft doesn’t clarify.
– “High-stakes decisionmaking” (§ 1(f)(2)) is undefined; agencies will need to delineate scope.
– § 2 (lines 14–16) states: “It is the intent of the Legislature to enact legislation that would relate to strengthening, establishing, and promoting the rights and values described in Section 1.” In other words, SB 420 is a blueprint, not a self-executing statute.
In sum, SB 420 erects a detailed framework of rights and obligations around AI deployment, but it defers most definitional work and enforcement design to future implementing legislation.
Senate - 468 - High-risk artificial intelligence systems: duty to protect personal information.
Legislation ID: 25832
Bill URL: View Bill
Sponsors
Senate - 524 - Law enforcement agencies: artificial intelligence.
Legislation ID: 25896
Bill URL: View Bill
Sponsors
Senate - 53 - Artificial intelligence: frontier models.
Legislation ID: 25601
Bill URL: View Bill
Sponsors
Senate - 579 - Mental health and artificial intelligence working group.
Legislation ID: 25945
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of SB 579. All quotations are drawn from the bill as introduced.
SECTION A: Definitions & Scope
1. “Artificial intelligence” (AI) is not explicitly defined in a standalone definitions section. However, the term recurs throughout, implicitly covering “AI-driven therapeutic tools, virtual assistants, diagnostics, and predictive models.”
– Citation: “(2) The current and emerging artificial intelligence technologies that have the potential to improve mental health diagnosis, treatment, monitoring, and care. The evaluation shall include artificial-intelligence-driven therapeutic tools, virtual assistants, diagnostics, and predictive models.” (Gov. Code § 12817(a)(2))
2. “Mental health settings” is not formally defined but is used in the mandate to evaluate “concerns regarding artificial intelligence in mental health settings.”
– Citation: “(1) The role of artificial intelligence in improving mental health outcomes, ensuring ethical standards, promoting innovation, and addressing concerns regarding artificial intelligence in mental health settings.” (Gov. Code § 12817(a)(1))
Scope statement
– The entire working group’s charge (Gov. Code § 12817(a)–(d)) is scoped to “evaluate” AI technologies as they relate to mental health and to “produce a report” by July 1, 2028. No direct exemptions or carve-outs are provided.
SECTION B: Development & Research
While SB 579 does not appropriate research funding or mandate data-sharing, it does:
1. Require an evaluation of “current and emerging AI technologies” with mental-health applications.
– Impact: Research institutions and startups working on AI for mental health may see elevated visibility and potential referral to pilot programs.
– Citation: “(2) The current and emerging artificial intelligence technologies… The evaluation shall include AI-driven therapeutic tools, virtual assistants, diagnostics, and predictive models.” (Gov. Code § 12817(a)(2))
2. Mandate “input from a broad range of stakeholders” including academic institutions.
– Impact: Academics may be formally solicited for white papers or workshops, increasing collaboration between universities and state government.
– Citation: “(c)(1) The working group shall take input from a broad range of stakeholders… This input shall come from groups, including, but not limited to, health organizations, academic institutions, technology companies, and advocacy groups.” (Gov. Code § 12817(c))
SECTION C: Deployment & Compliance
SB 579 stops short of imposing certification or liability rules. Instead it calls for policy recommendations:
1. Best practices and recommendations for facilitating beneficial uses and mitigating risks of AI in mental health.
– Impact: Could lead to future regulations or voluntary certification schemes based on the report’s recommendations. Vendors and practitioners will need to track those policy outcomes to ensure compliance.
– Citation: “(d)(2) This report shall include best practices and recommendations for policy around facilitating the beneficial uses and mitigating the potential risks surrounding artificial intelligence in mental health treatment.” (Gov. Code § 12817(d)(2))
2. A framework for training mental health professionals to incorporate AI tools effectively.
– Impact: Professional licensing boards may eventually require continuing education on AI; vendors of AI tools must provide training curricula or documentation aligned to that framework.
– Citation: “(d)(3) The report shall include a framework for developing training for mental health professionals to enhance their understanding of artificial intelligence tools and how to incorporate them into their practice effectively.” (Gov. Code § 12817(d)(3))
SECTION D: Enforcement & Penalties
SB 579 contains no enforcement mechanisms, fines, or criminal liabilities tied directly to AI usage.
– Members serve “without compensation” but “shall be reimbursed for all necessary expenses” (Gov. Code § 12817(e)); no penalties are prescribed for non-performance.
– Because the bill is purely evaluative and reporting in nature, any enforcement would depend on future legislation that acts on the working group’s recommendations.
SECTION E: Overall Implications
1. Advancing research and collaboration. By institutionalizing a working group with experts across mental health, AI, ethics, law, patient advocacy, and government, SB 579 promotes cross-sector dialogue. Academic researchers and startups are likely to benefit from an official channel into state policymaking.
2. Laying groundwork for future regulation. Though SB 579 does not itself regulate AI products, its mandated report (due July 1, 2028) will likely recommend standards, training requirements, or risk-mitigation strategies. That report could serve as the basis for subsequent rulemaking or statutory requirements affecting AI developers, vendors, and providers.
3. Emphasis on ethics and patient safety. By requiring evaluation of “unintended consequences” and “privacy concerns” (Gov. Code § 12817(a)(3)), the bill signals that California is preparing to address the unique ethical and safety risks posed by AI in sensitive care settings.
4. Ambiguities to watch. The absence of a formal definition of “AI” leaves room for debate over which tools fall under the working group’s remit. Likewise, “mental health settings” could be interpreted broadly to include peer-support apps or narrowly to cover only licensed clinical environments. These ambiguities may influence the scope of stakeholder input and the final recommendations.
In sum, SB 579 does not immediately constrain AI innovation but rather establishes a structured, multi-disciplinary review process that will inform California’s next steps on regulating, standardizing, and safely deploying AI in mental health care.
Senate - 7 - Artificial intelligence.
Legislation ID: 25559
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a structured analysis of the full text of Senate Bill No. 7 (“SB 7”), as introduced December 2, 2024. Because the bill in its present form contains only an intent statement and no operative definitions, obligations, or enforcement mechanisms, most of the sections below note the absence of substantive AI provisions.
Section A: Definitions & Scope
1. AI-Related Definitions
– SB 7 contains no definitions of “artificial intelligence,” “AI system,” “machine learning,” or any related term.
– Citation: SB 7, Sec. 1 (“It is the intent of the Legislature to enact legislation relating to artificial intelligence.”). No further definitions appear.
2. Scope Statements
– The only scope-setting language is the legislative intent to “enact legislation relating to artificial intelligence.”
– This broad statement (SB 7, Sec. 1) does not specify whether it will cover research, deployment, data, privacy, safety, or any particular AI domain.
Section B: Development & Research
– There are no clauses in SB 7 addressing research funding, pilot programs, university partnerships, data-sharing requirements, or reporting by AI labs or researchers.
– Absence of development provisions means SB 7 grants no new mandates or incentives for R&D.
Section C: Deployment & Compliance
– SB 7 does not impose any compliance requirements on commercial AI systems, vendors, or end-users.
– There is no certification process, auditing requirement, transparency rule, or liability framework included.
Section D: Enforcement & Penalties
– The bill includes no enforcement mechanism, penalty schedule, or private right of action.
– Because SB 7 does not create any substantive obligations, there is nothing to enforce.
Section E: Overall Implications
1. Immediate Effect
– At present, SB 7 is purely aspirational, expressing the Legislature’s intent to address AI in a future measure.
– It creates no new rights, duties, or regulatory bodies.
2. Signaling and Next Steps
– By introducing an “intent” bill, the Legislature signals that AI is on its agenda for the 2025 session.
– Stakeholders (research institutions, startups, established vendors, advocacy groups) should monitor forthcoming companion legislation that fills in definitions, sets policies, or allocates resources.
– Regulators and the Department of Technology (already established under existing law) may begin preparatory work—e.g., stakeholder outreach or baseline studies—but have no new statutory authority from SB 7 itself.
3. Ambiguities and Interpretations
– The phrase “relating to artificial intelligence” is very broad. It could encompass privacy, safety, workforce impacts, government procurement, ethics, or public-sector uses.
– Because the bill does not narrow this scope, the actual subject matter of future bills remains undefined.
Conclusion
SB 7 in its current form contains only a single operative sentence expressing legislative intent. It does not define AI or establish any substantive framework for research, deployment, compliance, or enforcement. Its primary value is as a placeholder indicating that AI-related legislation is forthcoming.
Senate - 82 - Public higher education: artificial intelligence usage.
Legislation ID: 145231
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an analysis organized as requested. Because SCR 82 is a non-binding resolution rather than a statute, many of the typical sections (funding mandates, penalties, certification requirements) are absent. I have nevertheless followed your structure, cited exact language from the text, and noted where provisions are silent or ambiguous.
Section A: Definitions & Scope
1. No explicit definitions.
– The resolution never defines “artificial intelligence” or variants such as “AI system,” “machine learning,” etc.
– Implication: Each campus segment may use its own definition, leading to potential inconsistency.
2. Implicit scope statements:
– “review the use of artificial intelligence in higher education” (lines 12–13).
– “strategies and best practices for … acceptable use of artificial intelligence” (lines 15–18).
– These clauses establish that “AI” is the subject matter but do not delimit what counts as AI.
Section B: Development & Research
SCR 82 contains no provisions directly funding or mandating AI research; rather, it:
1. Encourages formation of a “workgroup of faculty, staff, and administrators” (lines 12–13).
– Relevance: aims to gather academic subject-matter experts around AI.
– Impact: could stimulate interdisciplinary research discussions, but without dedicated funding or formal research protocols.
2. Calls for “collaborat[ion] … with experts in artificial intelligence” (lines 7–8 on page 3).
– Relevance: invites outside AI specialists to inform the workgroup.
– Ambiguity: “experts” is undefined—could be industry, academic researchers, vendors, or consultants.
3. No data-sharing rules, IP clauses, or reporting requirements beyond the final “report” (lines 15–16 on page 3).
Section C: Deployment & Compliance
This resolution does not impose compliance requirements or certification regimes. Instead, it asks the workgroup to:
1. “discuss strategies and best practices for acceptable use of artificial intelligence across the three segments” (lines 19–21).
2. “discuss strategies and best practices … including, but not limited to, mitigating plagiarism and ethically using artificial intelligence in academic assignments” (lines 23–26).
3. “discuss strategies and best practices for using artificial intelligence as it relates to providing student academic support” (lines 28–30).
4. “discuss and strategize on ways to provide professional support to professors on recognizing the use of artificial intelligence in student work, including reliable technologies for checking student work” (lines 35–38).
– Impact: could lead to voluntary guidelines or recommended tools (e.g., plagiarism checkers), but no mandate to adopt.
– Ambiguity: “reliable technologies” is unspecified—could be software, services, or manual review protocols.
Section D: Enforcement & Penalties
There are no enforcement mechanisms or penalties in this resolution.
– It is expressly non-binding (“encourages” rather than “requires”).
– No mention of sanctions, compliance audits, or disciplinary procedures.
– The only “deliverable” is a public report of agreed-upon strategies (lines 15–16 on page 3).
Section E: Overall Implications
1. Non-binding Guidance: By framing its provisions as “encourage” (lines 9–10) and “should discuss” (lines 15, 19, 23 etc.), the resolution lacks enforceable power. Implementation depends entirely on voluntary engagement by campus leadership.
2. Coordinated Academic Response: If acted upon, the resolution could foster cross-segment academic policies on AI use, promoting consistency across UC, CSU, and CCC.
3. Potential for Fragmentation: Absence of definitions and mandatory standards may lead each segment (and even each campus) to adopt divergent approaches unless the workgroup achieves strong consensus.
4. Limited Reach Beyond Higher Ed: The resolution envisions collaboration “with individuals who work in higher education outside of California,” but it does not extend to K–12, private institutions, or non-academic AI applications.
5. No Direct Impact on Commercial or Research Funding: Because there are no funding or procurement mandates, the resolution is unlikely to shift state AI R&D investments or alter vendor contracting.
In summary, SCR 82 is a framework for voluntary dialogue around AI use in California’s public higher education. It establishes an inclusive workgroup and calls for a public report on best practices, but it stops short of defining AI, mandating standards, or creating enforcement mechanisms. Its real-world impact will depend on how vigorously campus leaders and academic senates pursue the recommended discussions and whether they translate recommendations into binding campus policies.
Senate - 833 - Artificial intelligence: critical infrastructure.
Legislation ID: 26155
Bill URL: View Bill
Sponsors
Hawaii
Senate - 1384 - Artificial Intelligence; Advisory Council; Economic Development; Workforce; Labor; Education; Policy; Action Plan; Report; Appropriation; Positions
Legislation ID: 28002
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused breakdown of H.B. 1384, organized per your requested sections. Every claim is anchored to the bill’s text; where language is vague, I’ve noted possible interpretations.
Section A: Definitions & Scope
1. Purpose of the Council (§27-)
• “The purpose of the Hawaii artificial intelligence advisory council is to…advise the legislature to guide awareness, education, policy, and usage of artificial intelligence in the State…”
– Relevance: Frames the council as the State’s central AI policy body.
– Impact: Creates a permanent locus for AI strategy, ensuring AI is a stand-alone policy priority rather than buried under broader IT or economic development councils.
2. Definition of AI (§27-)
• “‘Artificial intelligence’ means models and systems capable of performing functions generally associated with human intelligence, including reasoning and learning.”
– Relevance: Establishes a broad, capability-based definition; covers everything from rule-based expert systems (reasoning) to machine learning.
– Ambiguity: “Generally associated with human intelligence” could be read to include any automation that mimics decision-making—potentially sweeping in basic automation. A narrower definition (e.g., neural networks, deep learning) would limit scope; this one is expansive.
3. “Advisory council” (§27-)
• “‘Advisory council’ means the Hawaii artificial intelligence advisory council.”
– Relevance: Ties all subsequent obligations and powers to this body.
Section B: Development & Research
1. Annual Action Plan (§27- Purpose)
• “recommend an action plan, updated annually as necessary…”
– Relevance: Mandates ongoing strategic planning for AI.
– Impact on researchers/startups: Creates predictable opportunities for input and may guide grant programs and public-private R&D collaborations.
2. Reports & Studies (§Section 2)
a. Initial Status Report due 20 days before 2026 session
– “the advisory council shall submit … a status report on the council’s activities and progress no later than twenty days prior to … 2026.”
– Impact: Forces early benchmarking; regulators and legislators get visibility into ongoing AI work.
b. December 31, 2026 Report (§2(c))
– Must cover “the current state of artificial intelligence and its likely impact on the State’s labor market” and recommend “legal regulations or policy changes…ethical use,” plus “ways to encourage AI innovation and entrepreneurship.”
– Relevance: Directs the council to map out workforce needs, legal architecture, and entrepreneurship incentives.
– Impact on academia & startups: Likely to spur educational program funding, seed-funding schemes, and regulatory sandboxes.
c. December 31, 2027 Final Report (§2(d))
– “Principles and values… governance framework… risk analysis… recommendations for supporting state and county government employees…”
– Impact: Lays groundwork for formal AI governance policies (data sharing, privacy, auditing) in state agencies.
3. Staffing & Consultants (§2(e)–(f))
• Establishes two permanent FTEs (1 analyst, 1 clerical) and authorizes procurement of consultants under HRS 103D.
– Relevance: Guarantees in-house expertise plus external specialists.
– Impact on consultants: Opens contract opportunities for AI policy experts, legal analysts, economists.
Section C: Deployment & Compliance
1. Action Plan Elements (§2(a))
• “(1) Competitively position the State…full economic benefits from AI; (2) Responsibly use AI to improve the efficiency of state and local government services.”
– Relevance: Directs dual focus on economic development and operational uptake of AI in government.
– Impact on end-users (public): Could lead to AI-powered service portals, automated permitting, chatbots for citizen inquiries.
2. Governance Framework (§2(d)(2))
• “A governance framework with policies, procedures, and processes for the development, deployment, and use of artificial intelligence by the State and county governments.”
– Relevance: Signals forthcoming guidelines—e.g., vendor vetting, risk assessments, transparency requirements, privacy safeguards.
– Impact on vendors: May need to comply with state-issued AI policy playbooks, risk-assessment templates, and reporting standards.
3. Risk Analysis (§2(d)(4))
• “A risk analysis of potential threats to the State’s key infrastructure from artificial intelligence technologies.”
– Relevance: Ensures security-focused review (e.g., adversarial attacks, data poisoning).
– Impact on regulators: May lead to mandatory penetration tests or third-party audits for critical systems.
Section D: Enforcement & Penalties
– The bill creates no direct enforcement mechanisms or civil/criminal penalties for non-compliance. All provisions hinge on the Advisory Council’s voluntary cooperation and reporting.
– Incentives: Implicit in recommended policies may be future grant programs, preferential procurement, or regulatory safe harbors for compliant AI vendors.
Section E: Overall Implications
1. Centralized AI Policy Hub
– By convening top executives (CIO, Director of Finance, Attorney General, etc.) plus technologists, academics, and industry, the Council bridges silos across government, education, and private sector.
2. Roadmap for AI Adoption
– The staged reports (2026, 2027) and defined deliverables (governance framework, risk analysis, workforce recommendations) will almost certainly shape procurement rules, ethics guidelines, and training curricula.
3. Economic Development Focus
– Explicit charge to “competitively position the State” and support “AI innovation and entrepreneurship” (Sec. 2(c)(5)) indicates forthcoming incentives—tax credits, incubators, public-private partnerships.
4. Workforce & Education
– Analysis of “labor market conditions” and “foundational skillsets” (Sec. 2(c)(1)–(2)) will likely inform K–12 STEM programs, university research grants, and adult reskilling initiatives.
5. Light Regulatory Touch—So Far
– The bill delegates substantive rulemaking to the Council; for the moment, it imposes no binding AI regulations on private entities. But the Council’s 2027 governance framework could lead to enforceable agency-level policies.
In sum, H.B. 1384 does not yet regulate AI products or punish violations. Instead, it builds the organizational and analytic infrastructure for Hawaii to understand, govern, and capitalize on AI—paving the way for future rules, incentives, and standards.
Senate - 1622 - UH; Aloha Intelligence Institute; Artificial Intelligence; Appropriations
Legislation ID: 29727
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. “Artificial intelligence” (AI) is not separately defined in the bill, but the bill repeatedly invokes “artificial intelligence” and related technologies. For example:
• “artificial intelligence technologies are rapidly transforming industries…” (Findings, lines 1–3).
• “establishing an artificial intelligence institute in Hawaii…” (Findings, lines 4–5).
Absence of a formal definition leaves scope ambiguous. It could be read broadly to encompass machine learning, natural language processing, robotics, data-analytics platforms, or any system claiming autonomous learning or decision-making.
2. Geographic and institutional scope:
• “artificial intelligence institute within the university of Hawaii” (Section 1, Purpose).
The institute’s jurisdiction is confined to the State of Hawaii and operates under the University of Hawaii system.
Section B: Development & Research
1. Dedicated R&D funding and positions
• Appropriations: “There is appropriated… $2,000,000… for fiscal year 2025-2026… $1,500,000 for ten full-time equivalent (10.0 FTE) permanent positions; and $500,000 for seven full-time equivalent (7.0 FTE) non-recurring positions…” (Section 3).
• Repeated for FY 2026-2027 (Section 4).
Impact: Provides stable funding stream and talent pipeline for AI R&D. Researchers gain salaried positions (“faculty of practice”) and startup resources.
2. Research focus areas
• “Facilitate interdisciplinary research and development in artificial intelligence with a focus on areas relevant to Hawaii, including but not limited to: (A) Climate resilience… (B) Sustainable agriculture… (C) Health care… (D) Renewable energy… (E) Creative media… (F) Advanced manufacturing… (G) Cultural and linguistic preservation.” (New § 304A-__.b(1)).
Impact: Directs R&D efforts toward local priorities. Could accelerate domain-specific AI solutions but may deprioritize other areas (e.g., finance, general-purpose AI).
3. Reporting requirements
• “The institute shall submit a biannual report to the legislature and the governor. The report shall include: (1) A summary of activities and achievements; (2) Financial statements and funding updates; and (3) Recommendations for future initiatives and funding needs.” (New § 304A-__.e).
Impact: Enhances transparency, enabling legislators to assess outcomes and adjust funding. May impose administrative burden on institute staff.
Section C: Deployment & Compliance
1. Ethical guidelines
• “Develop ethical guidelines and policies for the use of artificial intelligence in Hawaii.” (New § 304A-__.b(4)).
Impact: Sets expectation that AI deployments statewide adhere to ethics frameworks. However, no further detail on compliance mechanisms, certification, or auditing authority. Ambiguity: It is unclear whether guidelines will be advisory or carry enforceable weight.
2. Public-private partnerships
• “Partner with public and private entities to promote innovation, entrepreneurship, and job creation in artificial intelligence-related fields.” (New § 304A-__.b(3)).
Impact: Encourages startups and established vendors to co-develop solutions. Could streamline commercialization but lacks specifics on IP ownership, data-sharing obligations, or procurement preferences.
Section D: Enforcement & Penalties
The bill contains no explicit enforcement mechanisms, penalties, or incentives tied to failure to follow institute guidelines or ethical policies. All provisions relating to operations (reports, guidelines, partnerships) are nondisciplinary and lack sanctions for noncompliance.
Section E: Overall Implications
1. Advancement: By establishing a dedicated institute with stable funding, dedicated staff, and mandated reporting, the bill likely accelerates AI research tailored to Hawaii’s environmental, cultural, and economic needs.
2. Directional focus: The enumerated research domains (climate resilience, sustainable agriculture, cultural preservation) signal priority areas, potentially diverting resources from more generic AI research.
3. Workforce development: Mandated degree programs, training for faculty, K-12 outreach, and “student pathways” (New § 304A-__.b(2)) can expand the local AI talent pool and startup ecosystem.
4. Governance gap: While ethics guidelines are required, absence of enforcement or certification provisions may limit their real-world impact. The bill assumes voluntary compliance rather than regulated accountability.
5. Equity and inclusion: Inclusion of “Native Hawaiian and cultural experts” on the advisory board (New § 304A-__.c(4)) could foster culturally sensitive AI applications, though the mechanism for balancing technical and cultural priorities is unspecified.
In sum, the bill is structured primarily as an enabling act for research, education, and coordination, rather than a regulatory vehicle imposing binding rules on AI systems or vendors. Its major effect will be to create institutional capacity and funding within the University of Hawaii system to drive AI initiatives aligned with state economic and cultural objectives.
Senate - 194 - Revised 2025 Hawaii Patient Bill of Rights
Legislation ID: 136168
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of the “Revised 2025 Hawaii Patient Bill of Rights,” organized in the five sections you requested. Wherever possible, I quote or cite the exact provisions.
SECTION A: DEFINITIONS & SCOPE
1. “AI or Automated Decision System”
– Text: “AI or Automated Decision System: Any algorithmic or software-based platform that can autonomously generate or recommend coverage determinations without direct human supervision.”
– Why it matters: This definition explicitly brings AI into the scope of insurance coverage decision-making. By singling out any “autonomously generate” system, it covers both rule-based automation and more advanced machine-learning models.
2. HIPAA-equivalent Security
– Text: “HIPAA-equivalent Security: A standard of data protection meeting or exceeding requirements set forth in 45 C.F.R. Parts 160 and 164 (HIPAA Privacy and Security Rules).”
– Relevance to AI: Applies uniformly to any AI systems that store or process protected health information (PHI), ensuring they meet federal-level safeguards.
3. Urgent vs. Non-Urgent Requests
– Text: “Urgent requests are those where delays could seriously jeopardize a patient’s health…; non-urgent requests include all other prior authorizations not qualifying as urgent.”
– Implicit AI nexus: Later sections require AI decisions for both urgent and non-urgent categories, so this scope statement determines which workflows the AI oversight rules cover.
SECTION B: DEVELOPMENT & RESEARCH
This Bill of Rights contains no direct R&D funding mandates or grant programs for AI research. However, two provisions touch on research data or performance metrics:
1. Data Tracking Requirements (Section 8.3)
– Text: “Insurers must compile and submit monthly data on prior authorization approval/denial rates, average processing times, and the percentage of AI-based denials overturned on appeal.”
– Impact on research: Makes insurers’ AI decision outcomes measurable and transparent, potentially enabling academic or policy researchers to analyze AI fairness and accuracy over time.
2. Multidisciplinary Advisory Group (Section 10.3)
– Text: “Composed of physicians, cybersecurity experts, patient advocates, telehealth specialists, and others. Convenes periodically to review compliance, recommend updates, and study emerging issues (e.g., advanced AI, new data-security threats).”
– Research implications: Creates a forum where ongoing AI performance and safety can be evaluated, potentially steering future research priorities or standards.
SECTION C: DEPLOYMENT & COMPLIANCE
1. Mandatory Human Oversight (Section 8.2)
– Text: “If AI or an automated decision system initiates a denial, that denial must be reviewed and co-signed by a board-certified specialist in the relevant field before being finalized.”
– Analysis: This directly restricts fully automated denial workflows. Startups or vendors offering “AI-only” coverage decision tools would need to integrate expert-in-the-loop processes.
2. AI Usage Notification (Section 8.2)
– Text: “Patients and providers shall be notified in writing when AI is used at any stage of the coverage determination.”
– Impact: Promotes transparency but also imposes additional compliance work—systems must log and trigger notifications whenever an AI decision is invoked.
3. Data Offshoring Accountability (Section 9.2)
– Text: “Prior to offshoring data, an entity must file an attestation with the Insurance Commissioner confirming that any overseas subcontractors adhere to encryption, breach notification, audit logging, and confidentiality protocols.”
– AI relevance: Applies to any AI vendor processing PHI offshore, ensuring that AI-training data and model hosting abroad meet HIPAA-level safeguards.
4. Breach Notification and Penalties (Section 9.3)
– Text: “In the event of a suspected or actual data breach, the entity must notify affected patients and the Insurance Commissioner within 72 hours, implementing a corrective action plan.”
– AI systems often rely on large PHI datasets; this tight deadline increases operational risk for AI vendors handling that data.
SECTION D: ENFORCEMENT & PENALTIES
1. Authority of the Insurance Commissioner (Section 10.1)
– Text: “Empowered to audit, investigate, and enforce all provisions of this Bill of Rights. May impose fines, clawbacks, revocation of accreditation, and other appropriate remedies for noncompliance.”
– AI angle: The Commissioner can sanction both insurers and AI vendors who integrate non-compliant AI decision tools, or who fail to meet the human-oversight and notification requirements.
2. Annual Public Report (Section 10.2)
– Text: “The Insurance Commissioner shall publish an annual report detailing enforcement actions, complaint data, AI usage rates, denial statistics, and any data breaches or security infractions.”
– Effect on the ecosystem: Creates a public data stream on how often AI is used, its error or reversal rates, and any security incidents—fuel for policy debate and vendor benchmarking.
3. Anti-Retaliation (Section 11.1)
– Text: “Insurers, health plans, or affiliated entities shall not retaliate against providers… for… participating in external reviews concerning the insurer’s compliance with this Bill of Rights.”
– AI implication: Protects clinicians or staff who speak up about flawed AI denials, helping uncover systemic AI harms without fear of contract termination.
SECTION E: OVERALL IMPLICATIONS FOR HAWAII’S AI ECOSYSTEM
• Increased Compliance Overhead: Startups and established AI vendors must build human-in-the-loop review modules, notification systems, and detailed logs. These raise development costs but may improve patient safety.
• Transparency & Accountability: Mandatory tracking of AI denial rates and overturns, plus annual public reporting, will shine a light on AI performance—encouraging more responsible AI deployment.
• Barrier to Fully Automated Systems: The co-sign requirement effectively bans “black-box” denial tools without expert oversight, slowing adoption of purely automated underwriting or claims-processing engines.
• Data Residency and Security: The offshoring attestations and 72-hour breach notice limit where and how AI models can be trained on Hawaii patient data, favoring vendors with robust global compliance capabilities.
• Forum for Ongoing Improvement: The multidisciplinary advisory group and follow-up review timeline create a mechanism to adapt AI governance as technologies evolve, positioning Hawaii to refine its rules in light of new risks or breakthroughs.
In sum, while this resolution does not directly fund AI research, it establishes strict guardrails for any AI-driven coverage determinations—prioritizing patient safety, transparency, and human oversight. This approach is likely to slow down unfettered AI deployment in health insurance but may foster higher-quality, more trustworthy AI tools over time.
Senate - 202 - Revised 2025 Hawaii Patient Bill of Rights
Legislation ID: 135988
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a structured, citation-anchored analysis of all the AI-relevant material in H.C.R. No. 202 (the “Revised 2025 Hawaii Patient Bill of Rights”). For each major provision, I explain why it pertains to AI, discuss its likely impact on stakeholders, and quote the precise text that supports the analysis.
Section A: Definitions & Scope
1. “AI or Automated Decision System”
– Text: “AI or Automated Decision System: Any algorithmic or software-based platform that can autonomously generate or recommend coverage determinations without direct human supervision.” (Foreword and Definitions, item 2)
– Why it matters: Establishes the range of systems subject to the bill—both proprietary and off-the-shelf algorithms used by insurers or third-party administrators to approve or deny claims.
– Impact: Insurers must inventory and track all such systems, and regulators will need to interpret “autonomously” and “without direct human supervision,” leaving room for debate around “human-in-the-loop” designs.
2. HIPAA-equivalent Security
– Text: “HIPAA-equivalent Security: A standard of data protection meeting or exceeding requirements set forth in 45 C.F.R. Parts 160 and 164 (HIPAA Privacy and Security Rules).” (Foreword and Definitions, item 2)
– Why it matters: Applies to any system—AI included—that stores/transmits protected health information.
– Impact: AI developers/offshore vendors must certify compliance; ambiguous boundary between “meeting” vs. “exceeding” could spur regulatory guidance or litigation.
Section B: Development & Research
This resolution focuses on operational use of AI by insurers rather than on state-funded AI R&D or data-sharing for innovation. No clauses mandate new AI research grants or public-sector data sharing for model development.
Section C: Deployment & Compliance
1. AI Oversight in Prior Authorization
– Text: “8.2 AI Oversight:
o If AI or an automated decision system initiates a denial, that denial must be reviewed and co-signed by a board-certified specialist in the relevant field before being finalized.
o Patients and providers shall be notified in writing when AI is used at any stage of the coverage determination.”
(Section 8.2)
– Why it matters: Directly governs deployment of AI in the claims-adjudication workflow. Requires human specialist sign-off and explicit notice.
– Impact:
• Insurers must redesign workflows to insert specialist review gates—raising costs, potentially slowing down approvals.
• Start-ups offering automated denial systems will face barriers to entry unless they partner with clinicians.
• Providers and patients gain transparency but may see longer turnaround times if specialists’ availability is limited.
2. Data Tracking and Reporting Requirements
– Text: “8.3 Data Tracking: Insurers must compile and submit monthly data on prior authorization approval/denial rates, average processing times, and the percentage of AI-based denials overturned on appeal.” (Section 8.3)
– Why it matters: Creates a new reporting mandate tied specifically to AI-driven decisions.
– Impact:
• AI vendors need to instrument their systems to log metadata.
• Regulators gain visibility into AI performance, facilitating audits or public reports.
• Smaller insurers may struggle with the technical overhead of monthly AI metrics reporting.
Section D: Enforcement & Penalties
1. Authority of the Insurance Commissioner
– Text: “10.1 Authority of the Insurance Commissioner:
o Empowered to audit, investigate, and enforce all provisions of this Bill of Rights.
o May impose fines, clawbacks, revocation of accreditation, and other appropriate remedies for noncompliance.” (Section 10.1)
– Why it matters: Gives regulators teeth to police AI misuse.
– Impact:
• Insurers face real financial and operational risk for AI violations—driving them toward conservative, well-documented AI deployments.
• Vendors will need compliance certifications (e.g., SOC 2, ISO 27001) to assure purchasers.
2. Breach Notification and Penalties for Data Offshoring
– Text: “9.3 Breach Notification and Penalties: In the event of a suspected or actual data breach, the entity must notify affected patients and the Insurance Commissioner within 72 hours … Repeated or willful violations may result in fines, revocation of accreditation, or other sanctions.” (Section 9.3)
– Why it matters: Applies equally to AI systems that handle PHI offshore.
– Impact:
• Offshore AI service providers will face the same 72-hour breach window as domestic ones.
• Heightened risk may shift offshore workloads back onshore or into hybrid cloud models with stricter encryption.
Section E: Overall Implications
– Transparency & Accountability: By requiring disclosure whenever AI is used (8.2) and by mandating performance metrics (8.3), the state creates a high degree of oversight, likely slowing AI adoption initially but building trust among patients and providers.
– Human-in-the-Loop Preference: The co-sign-off rule will entrench models where clinicians remain central to coverage decisions, potentially dampening purely automated “AI blacklist” products.
– Compliance Burden: Small insurers, third-party administrators, and AI startups will face nontrivial build-out costs for audit logs, breach reporting, and HIPAA-equivalent safeguards, raising barriers to entry. Established vendors may gain advantage by offering turnkey compliance.
– Regulatory Authority: The Insurance Commissioner emerges as a powerful AI regulator in health insurance, with broad enforcement powers (10.1). This may serve as a model for other states but also risks uneven enforcement unless the department expands technical expertise.
Ambiguities & Open Questions
– “Without direct human supervision” in the AI definition could be interpreted narrowly (fully autonomous systems only) or broadly (any use of predictive algorithms).
– The phrase “HIPAA-equivalent Security” leaves flexibility on “exceeding” HIPAA requirements, which could fuel debates over acceptable encryption, data residency, or logging standards.
– The one-day and three-day turnaround targets for prior authorization (Section 8.1) may be unrealistic in rural contexts, potentially triggering waiver requests or rule amendments.
In sum, the Revised 2025 Hawaii Patient Bill of Rights does not directly fund AI research but contains several provisions—particularly in Section 8 (“Transparent and Timely Prior Authorization”) and Section 9 (“Data Protection and Privacy”)—that substantially regulate AI deployment in health insurance. These rules emphasize human oversight, transparency, data-security accountability, and robust enforcement, shaping a cautious but visible role for AI in Hawaii’s healthcare system.
Senate - 26 - Revised 2025 Hawaii Patient Bill of Rights
Legislation ID: 136437
Bill URL: View Bill
Sponsors
Senate - 28 - Revised 2025 Hawaii Patient Bill of Rights
Legislation ID: 136439
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of the Revised 2025 Hawaii Patient Bill of Rights. Each point is anchored to the exact language of the draft resolution.
SECTION A: DEFINITIONS & SCOPE
1. “AI or Automated Decision System” (Foreword – Definitions, ¶2)
• Quotation: “AI or Automated Decision System: Any algorithmic or software-based platform that can autonomously generate or recommend coverage determinations without direct human supervision.”
• Analysis: This definition explicitly brings any machine learning, rule-based engine, or “black-box” software under the Bill’s purview. By defining AI in the context of insurance coverage decisions, it ensures the entire text applies whenever an insurer uses an automated process.
2. “HIPAA-equivalent Security” (Foreword – Definitions, ¶2)
• Quotation: “HIPAA-equivalent Security: A standard of data protection meeting or exceeding requirements set forth in 45 C.F.R. Parts 160 and 164.”
• Analysis: Though not AI in itself, this scope ensures that any AI system processing personal health data must comply with HIPAA-level safeguards—critical for machine-learning models trained on patient records.
3. Foreword’s stated purpose (Foreword, ¶1)
• Quotation: “This Bill of Rights modernizes patient protections to address AI-based coverage decisions, data security risks, and ongoing provider shortages in Hawaii.”
• Analysis: AI-based coverage decisions are singled out alongside telehealth and provider shortages. The “purpose” clause makes clear that AI use by insurers is both expected and regulated.
SECTION B: DEVELOPMENT & RESEARCH
(No direct R&D/funding mandates in this draft. The text focuses on operational use of AI in insurance rather than on promoting AI research.)
SECTION C: DEPLOYMENT & COMPLIANCE
1. AI Oversight & Specialist Review (Section 8.2)
• Quotation: “If AI or an automated decision system initiates a denial, that denial must be reviewed and co-signed by a board-certified specialist in the relevant field before being finalized.”
• Analysis: This requirement imposes a human-in-the-loop guardrail on any automated coverage decision, slowing fully automated workflows but increasing transparency and accountability. It effectively restricts “deploy-and-run” AI systems, forcing insurers to build clinician review into their pipelines.
2. Mandatory AI Disclosure (Section 8.2)
• Quotation: “Patients and providers shall be notified in writing when AI is used at any stage of the coverage determination.”
• Analysis: Insurers must instrument their processes to flag and log AI usage, then surface that flag to end-users. This raises compliance costs—every system call to an AI service must be traceable.
3. AI-Denial Data Tracking (Section 8.3)
• Quotation: “Insurers must compile and submit monthly data on prior authorization approval/denial rates, average processing times, and the percentage of AI-based denials overturned on appeal.”
• Analysis: By requiring granular reporting on AI outcomes, the Bill incentivizes insurers to monitor model performance and error rates. It could spur the development of internal audit tools but also burden smaller carriers with compliance overhead.
SECTION D: ENFORCEMENT & PENALTIES
1. Offshoring Accountability (Section 9.2)
• Quotation: “Prior to offshoring data, an entity must file an attestation with the Insurance Commissioner confirming that any overseas subcontractors adhere to encryption, breach notification, audit logging, and confidentiality protocols.”
• Analysis: This indirectly governs AI-as-a-service providers located abroad. Any insurer using cloud-based AI translation, NLP, or decision-support systems outside the U.S. must pre-certify vendor security. Non-U.S. AI firms will face extra paperwork or random audits.
2. Breach Notification Timeline (Section 9.3)
• Quotation: “In the event of a suspected or actual data breach, the entity must notify affected patients and the Insurance Commissioner within 72 hours.”
• Analysis: AI vendors and insurers must instrument their models and data stores to detect anomalies quickly. Security-oriented logging and SIEM (Security Incident and Event Management) tools become mandatory to meet the tight timeline.
3. Insurance Commissioner Authority (Section 10.1)
• Quotation: “Empowered to audit, investigate, and enforce all provisions of this Bill of Rights… May impose fines, clawbacks, revocation of accreditation, and other appropriate remedies for noncompliance.”
• Analysis: This centralized enforcement gives regulators sweeping powers to inspect AI systems’ decision logs, model documentation, and audit trails. Noncompliant AI deployments could be fined or barred from Hawaii’s market.
SECTION E: OVERALL IMPLICATIONS
• Restrictive Compliance Overhead: By mandating human co-signatures, extensive disclosure, and monthly AI-outcome reporting, the Bill significantly raises the bar for any insurer wishing to deploy automated coverage decisions. Small startups may struggle with these administrative burdens.
• Improved Transparency & Accountability: Patients and providers gain clear rights to know when AI is in use, and regulators can quantify AI’s real-world impact via the required metrics. This could build public trust in AI while channeling innovation toward explainable, auditable systems.
• Vendor Impact: Offshore AI service providers—e.g., cloud-based ML or NLP APIs—must demonstrate HIPAA-equivalent safeguards and submit to audits. This may lead insurers to prefer domestic AI vendors or invest in on-premises solutions.
• Regulatory Precedent: The human-in-the-loop requirement and AI usage disclosure may serve as a model for other states. Established insurers with compliance teams may adapt more easily than leaner startups, potentially reinforcing market incumbents.
• Ambiguities Noted:
– “Co-signed by a board-certified specialist” (8.2) does not specify turnaround times for that human review—could be read as adding unlimited delay.
– The term “AI-based denial” (8.3) hinges on the insurer’s internal definition of what constitutes AI involvement, opening potential for under-reporting if insurers classify certain algorithmic checks as “rules-based” rather than AI.
In sum, the Bill clearly targets AI-driven insurance decisions, imposing transparency, human-oversight, security, and reporting requirements that will reshape how insurers—and the AI vendors who serve them—develop, deploy, and audit automated coverage-determination systems in Hawaii.
Senate - 43 - Revised 2025 Hawaii Patient Bill of Rights
Legislation ID: 136216
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of the Revised 2025 Hawaii Patient Bill of Rights. Every claim is anchored to the text you provided.
Section A: Definitions & Scope
1. “AI or Automated Decision System”
– Text (Definitions ¶2):
“AI or Automated Decision System: Any algorithmic or software-based platform that can autonomously generate or recommend coverage determinations without direct human supervision.”
– Analysis: This is the only definition that explicitly names “AI.” It covers any system that makes or recommends insurance coverage decisions automatically.
2. “HIPAA-equivalent Security”
– Text (Definitions ¶2):
“HIPAA-equivalent Security: A standard of data protection meeting or exceeding requirements set forth in 45 C.F.R. Parts 160 and 164 (HIPAA Privacy and Security Rules).”
– Analysis: While not AI-specific, this definition has bearing on AI systems that handle patient data offshore or onshore (see Section 9).
3. “Urgent vs. Non-Urgent”
– Text (Definitions ¶2):
“Urgent requests are those where delays could seriously jeopardize a patient’s health, life, or overall well-being; non-urgent requests include all other prior authorizations not qualifying as urgent.”
– Analysis: Distinguishing urgent vs. non-urgent is critical because the bill imposes different AI oversight timelines (see Section 8).
Section B: Development & Research
There are no provisions specifically funding or mandating AI R&D, nor requirements for data-sharing for the purpose of AI development. All AI references relate to oversight of insurer decision-making systems.
Section C: Deployment & Compliance
1. Mandatory Human Oversight of AI Decisions
– Text (8.2 AI Oversight):
“If AI or an automated decision system initiates a denial, that denial must be reviewed and co-signed by a board-certified specialist in the relevant field before being finalized. Patients and providers shall be notified in writing when AI is used at any stage of the coverage determination.”
– Impact: Insurers cannot rely solely on AI to deny claims—they must have a qualified human expert review every AI-generated denial. This slows down fully automated systems and increases labor costs, but may improve fairness and reduce adverse events.
2. AI Usage Disclosure
– Text (8.2 AI Oversight):
“Patients and providers shall be notified in writing when AI is used at any stage of the coverage determination.”
– Impact: Creates transparency obligations. Every time an insurer’s workflow touches an AI system for coverage decisions, they must inform the enrollee. Start-ups and vendors must build notification features into their products.
3. AI Denial Metrics Reporting
– Text (8.3 Data Tracking):
“Insurers must compile and submit monthly data on prior authorization approval/denial rates, average processing times, and the percentage of AI-based denials overturned on appeal.”
– Impact: Regulators will track how often AI-made denials occur and get overturned, potentially identifying biased or unreliable models. This could pressure vendors to improve AI accuracy or risk regulatory scrutiny if overturn rates are high.
4. Data Offshoring Accountability (Relevant to AI service providers)
– Text (9.2 Offshoring Accountability):
“Prior to offshoring data, an entity must file an attestation with the Insurance Commissioner confirming that any overseas subcontractors adhere to encryption, breach notification, audit logging, and confidentiality protocols. Entities shall undergo random audits or produce security certifications upon request.”
– Impact: AI vendors offering offshore data processing must prove compliance with HIPAA-equivalent controls. This raises entry barriers for small offshore AI firms lacking formal security certifications.
Section D: Enforcement & Penalties
1. Insurance Commissioner Authority
– Text (10.1 Authority of the Insurance Commissioner):
“Empowered to audit, investigate, and enforce all provisions of this Bill of Rights. May impose fines, clawbacks, revocation of accreditation, and other appropriate remedies for noncompliance.”
– Impact: The Commissioner can sanction insurers (and by extension any AI provider they contract with) for failure to implement required human review, notifications, or reporting.
2. Annual Public AI Usage Reporting
– Text (10.2 Annual Public Report):
“Shall publish an annual report detailing enforcement actions, complaint data, AI usage rates, denial statistics, and any data breaches or security infractions.”
– Impact: AI vendors’ market reputations will be influenced by publicly reported AI denial rates and breach incidents.
3. Multidisciplinary Advisory Group
– Text (10.3 Multidisciplinary Advisory Group):
“Composed of physicians, cybersecurity experts, patient advocates, telehealth specialists, and others. Convenes periodically to review compliance, recommend updates, and study emerging issues (e.g., advanced AI, new data-security threats).”
– Impact: Creates an ongoing forum where AI standards and best practices can evolve. Start-ups and researchers can engage to influence future rule-making.
Section E: Overall Implications for Hawaii’s AI Ecosystem
• Increased Human-in-the-Loop Requirements: Any AI application in insurance prior authorization must be paired with board-certified specialist review. This preserves patient safety but reduces opportunities for fully automated, cost-saving AI deployments.
• Transparency and Accountability: Mandatory AI usage notifications and overturn-rate reporting foster trust but impose compliance costs on insurers and AI vendors.
• Data Security Burden on Offshore Providers: The offshoring attestation and audit regime effectively favors vendors who already possess HIPAA-equivalent security posture, potentially disadvantaging smaller international AI firms.
• Regulatory Oversight and Evolution: The Insurance Commissioner’s broad enforcement powers plus a standing advisory group mean AI regulations in healthcare will continue to tighten and evolve in Hawaii.
• Ambiguities to Monitor:
– “Reviewed and co-signed” (8.2): It is unclear how much of the AI output must be reconsidered—does the specialist need to redo the entire decision or simply sign off?
– “AI usage rates” (10.2): The text does not define what counts as an “AI usage” (e.g., full decision vs. partial triage). Clearer metrics will be needed in guidance.
In sum, the bill does not encourage new AI R&D in healthcare, but it tightly governs any AI used in coverage determinations—mandating human review, full transparency, detailed reporting, and robust data-security measures. These provisions will reshape AI deployment in Hawaii’s insurance sector by raising compliance costs, improving patient protections, and requiring ongoing regulatory engagement.
Senate - 45 - Revised 2025 Hawaii Patient Bill of Rights
Legislation ID: 136218
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a focused analysis of the AI-relevant portions of the Proposed “Revised 2025 Hawaii Patient Bill of Rights,” organized into the five sections you requested. Every claim is anchored to verbatim text from the bill.
Section A: Definitions & Scope
1. Definition of “AI or Automated Decision System”
• Quotation (Foreword and Definitions, ¶2):
“AI or Automated Decision System: Any algorithmic or software-based platform that can autonomously generate or recommend coverage determinations without direct human supervision.”
• Analysis: This is the sole place in the text that defines “AI,” and it explicitly targets systems that make—or recommend—coverage decisions. By limiting the scope to “coverage determinations,” it signals that only AI embedded in insurance decision-making (not, say, diagnostics AI) is regulated here.
2. Reference to “AI-driven denials” and “AI accountability” (Whereas clauses)
• Quotation (Whereas, ¶3 and ¶6):
“recent increases in claims denials, particularly those driven by automated or artificial intelligence (AI)-based systems…”
“…prior authorization, AI accountability, and real enforcement…”
• Analysis: These preambles frame the overall crisis as one partly caused by AI-based denials, thus tying much of the regulatory focus to insurance-side AI.
Section B: Development & Research
(The bill contains no direct R&D funding or data-sharing mandates for AI beyond offshoring attestations—see below. There is no explicit support or grant language for AI research.)
1. Offshoring Security Attestation (9.2)
• Quotation: “Prior to offshoring data, an entity must file an attestation with the Insurance Commissioner confirming that any overseas subcontractors adhere to encryption, breach notification, audit logging, and confidentiality protocols.”
• Analysis: Though focused on data security, this indirectly affects AI teams that rely on off-shore data labeling or model training, by imposing an administrative hurdle (attestation) and potential audit.
Section C: Deployment & Compliance
1. AI Oversight in Prior Authorization (8.2)
• Quotation (8.2.a–b):
“If AI or an automated decision system initiates a denial, that denial must be reviewed and co-signed by a board-certified specialist in the relevant field before being finalized.”
“Patients and providers shall be notified in writing when AI is used at any stage of the coverage determination.”
• Analysis:
– This imposes a human-in-the-loop requirement. Startups or vendors supplying automated adjudication systems must redesign to insert a documented specialist sign-off.
– The mandatory disclosure clause may discourage “black-box” systems by forcing transparency about where AI is applied.
2. Data Tracking and Reporting (8.3)
• Quotation: “Insurers must compile and submit monthly data on prior authorization approval/denial rates, average processing times, and the percentage of AI-based denials overturned on appeal.”
• Analysis: Vendors and insurers must build logging and analytics features to tag which decisions were AI-based. This creates ongoing compliance costs and may disincentivize wholly automated workflows.
Section D: Enforcement & Penalties
1. Insurance Commissioner Authority (10.1)
• Quotation: “Empowered to audit, investigate, and enforce all provisions of this Bill of Rights. May impose fines, clawbacks, revocation of accreditation, and other appropriate remedies for noncompliance.”
• Analysis: Any AI provider integrated into an insurer’s workflow is subject to state investigation and sanction if it fails to meet the human-oversight or reporting obligations above.
2. Breach Notification and Penalties (9.3)
• Quotation: “In the event of a suspected or actual data breach, the entity must notify affected patients and the Insurance Commissioner within 72 hours … Repeated or willful violations may result in fines, revocation of accreditation, or other sanctions.”
• Analysis: AI platforms handling patient data—especially offshored components—must adhere to tight breach-reporting windows or face major penalties, adding risk to distributed AI development models.
Section E: Overall Implications
1. Advance vs. Restrict
– Restrictive: The human-in-the-loop and co-signing mandate (8.2.a) directly curtails fully autonomous AI adjudication systems, slowing deployment of end-to-end automation.
– Transparency: Mandatory disclosure of AI usage (8.2.b) and public reporting (8.3) push for explainability, which may drive vendors toward interpretable models.
– Compliance burden: Monthly reporting and attestation for offshoring (8.3; 9.2) will raise operational costs for both startups and established vendors, possibly favoring incumbents better able to absorb these costs.
2. Impact on Stakeholders
– Researchers: Little direct support; oversight and attestation rules may deter partnerships with overseas research labs or crowdsourced labelers.
– Startups: Increased compliance costs and the need to build human-review workflows may throttle innovation in automated coverage systems.
– Established Vendors: Better positioned to absorb the new audit, logging, and human-oversight requirements.
– Regulators: The Insurance Commissioner’s office gains broad new powers and data visibility, requiring increased staffing and technical expertise.
– End-Users (Patients/Providers): Likely more clarity about when AI is used, but potentially slower decisions if human co-signatures create bottlenecks.
3. Ambiguities
– Definition scope: The term “coverage determination” is not further defined—does it include payment adjudication, utilization management, or only pre-claim prior-auth?
– “Board-certified specialist” is not specified by certification body, leaving open questions about acceptable qualifications.
In sum, the bill explicitly targets AI as applied to insurance coverage decisions, imposing human-oversight, transparency, reporting, and security requirements. While it stops short of banning insurance AI outright, it raises the bar for deployment, particularly disadvantaging fully automated or offshore-only models.
Senate - 487 - Data and Artificial Intelligence Governance and Decision Intelligence Center; Established; Data Sharing; Appropriation
Legislation ID: 28603
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. “Data and artificial intelligence governance” (Section 2(c))
– Text: “ ‘Data and artificial intelligence governance’ means creating and implementing policies, standards, and tools to ensure availability, quality, security, privacy protection, and usability of data and artificial intelligence.”
– Analysis: This definition explicitly covers both data governance and AI governance. By linking AI to policies on “availability, quality, security, privacy,” the bill scopes AI systems under the same regulatory umbrella as state data.
2. “Decision intelligence” (Section 2(c))
– Text: “ ‘Decision intelligence’ means the use of machine learning-enabled and artificial intelligence-enabled data analytics and predictive models with visualized insights to provide decision recommendations, support operations, and track the impact of decisions.”
– Analysis: This term ties AI directly to providing “decision recommendations” and “predictive models,” signaling that any AI system deployed to assist state decision-making falls within the center’s purview.
3. “Integrated data and artificial intelligence platform” (Section 2(c))
– Text: “ ‘Integrated data and artificial intelligence platform’ means a comprehensive data management solution package, including machine learning and artificial intelligence capabilities, that enables agencies to gather, manage, analyze, visualize, and share data, as well as conduct analytics, predictions, and decision intelligence functions…”
– Analysis: By defining an “AI platform,” the statute implicitly regulates providers of end-to-end AI tooling adopted by state agencies.
4. “Open data” (Section 2(c))
– Text: “ ‘Open data’ means data that can be shared with the public for use and republication without restriction.”
– Analysis: Although not AI-specific, open data drives the “fuel” for AI models. Requiring certain datasets to be open may accelerate public-sector AI research and third-party innovation.
Section B: Development & Research
1. Statewide data sharing and interoperability (Section 2(b)(2))
– Text: “Enabling secured and efficient data sharing across state agencies through implementing statewide data sharing tools and platforms to improve interoperability.”
– Impact: By mandating “tools and platforms,” the bill could require development or procurement of secure data APIs, benefiting in-state AI startups offering data integration services.
2. Master data management for a “master citizen record” (Section 2(b)(4))
– Text: “Collaborating with agencies and utilizing master data management technology to create a statewide master citizen record to enable seamless citizen service and improve citizen experience.”
– Impact: Centralizing citizen records may provide a rich training corpus for state-sponsored AI research—though there is risk of privacy overreach if not governed tightly.
3. Team to support “data policies, data management, and data analytics” (Section 2(b)(6))
– Text: “Establishing a team to support agency implementation of data policies, data management, and data analytics to support agencies and decision makers in evidence-based decision making.”
– Impact: This provision effectively funds in-house AI expertise, facilitating research pilots within government but also setting standard consulting opportunities for local AI researchers.
4. Budget for AI architect, engineer, scientist, analyst (Section 4(4)–(7))
– Text examples:
• Data and AI architect: “$120,000 for one FTE… to design, develop, and optimize data and artificial intelligence models…” (4(4)).
• Data and AI scientist: “$120,000 for one FTE… to use statistics algorithms and machine learning models to interpret data and conduct predictive analytics…” (4(6)).
– Impact: Direct state funding of technical positions signals strong public-sector investment in AI R&D infrastructure. It may attract vendors or create competition for private AI talent.
Section C: Deployment & Compliance
1. AI governance policies and “access control” (Section 2(b)(3))
– Text: “Implementing data and artificial intelligence policies and statewide data and artificial intelligence governance tools to allow agencies to protect data through proper access control for secured use of data and artificial intelligence.”
– Impact: This implies a compliance framework for AI system deployment (e.g., identity management, role-based access). Vendors will need to ensure their systems integrate with state access-control policies.
2. Statewide “integrated data and AI platform” (Section 2(b)(5))
– Text: “Creating and managing a statewide integrated data and artificial intelligence platform to facilitate data sharing to enable analytics and decision recommendations.”
– Impact: Agencies may be required to deploy all AI workloads on the state’s chosen platform. This centralization could limit choice for startups or favor certain vendors, while ensuring uniform compliance.
3. Open data publication (amended Section 27-44(a))
– Text: “Each executive branch department… shall use reasonable efforts to make appropriate and existing data sets… available… provided that… proprietary… or information protected… shall not be disclosed.”
– Impact: Mandating open data supports AI model training and third-party innovation, but carve-outs for privacy/proprietary data introduce ambiguity. Startups may lobby for expanded data release; agencies may err on side of non-release to avoid risk.
Section D: Enforcement & Penalties
– The bill does not specify penalties or sanctions for non-compliance with AI governance. It relies on the chief data officer’s authority (“shall oversee”) and existing statutes (e.g., Chapter 92F for privacy).
– Ambiguity: Without explicit enforcement mechanisms or liability provisions for AI misuse, the center’s power is largely advisory and enabling, rather than punitive. This may lead to uneven adoption.
Section E: Overall Implications
1. Centralization of AI governance: Establishing a single center under the chief data officer ensures uniform policies, but risks creating a bottleneck or favoring certain technologies.
2. Growth of public-sector AI capacity: Funded specialist roles (architects, scientists, engineers) will accelerate state AI initiatives, creating demand for vendors and partnerships.
3. Data access vs. privacy tension: Open data mandates boost AI development, yet privacy carve-outs may slow data flow. Clearer guidelines will be needed to resolve ambiguities.
4. Vendor landscape: The “integrated data and AI platform” requirement may incentivize large platform providers; smaller startups could compete by offering add-ons or compliance modules.
5. Ambiguous enforcement: The lack of explicit penalties suggests the center will rely on collaboration and best practices rather than strict regulatory oversight—potentially offering a flexible but less enforceable model for AI governance.
Senate - 546 - Aloha Intelligence Institute; Artificial Intelligence; University of Hawaii; Appropriation
Legislation ID: 27174
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an analysis of H.B. 546 (“Aloha Intelligence Institute Act”) organized in the five requested sections. Every claim is anchored to quotations from the bill.
Section A: Definitions & Scope
1. Absence of a formal definition.
- The bill never defines “artificial intelligence,” “AI system,” or related technical terms. Instead, it repeatedly uses the phrase “artificial intelligence” without qualification—for example, “artificial intelligence technologies” (Findings, lines 1–2) and “artificial intelligence initiatives” (Purpose, line 26).
- Ambiguity: Because no statutory definition appears, “artificial intelligence” could be interpreted broadly (machine learning, expert systems, robotics, data analytics) or narrowly (only neural-network–based systems).
2. Scope statements naming AI as the institute’s focus.
- “There is established the aloha intelligence institute… The institute shall: (1) Facilitate interdisciplinary research and development in artificial intelligence…” (§ 304A--, subsecs. (a)–(b)(1)).
- This language makes clear that all R&D, training, partnerships, and ethics work under the new institute is explicitly centered on “artificial intelligence.”
Section B: Development & Research
1. Funding mandates and structure.
- “There is appropriated… $2,000,000… for fiscal year 2025-2026… $1,500,000 for ten full-time equivalent… permanent positions; and $500,000 for… non-recurring positions and start-up expenses” (Sec. 3).
- Similarly, Sec. 4 appropriates another $2 million for FY 2026–2027. These appropriations directly allocate state dollars to hire faculty and staff whose activities will advance AI research and education.
2. Research focus areas.
- The bill lists seven strategic domains “relevant to Hawaii”: climate resilience, sustainable agriculture, health care, renewable energy, creative media, advanced manufacturing, and “cultural and linguistic preservation” (§ 304A--, subsec. (b)(1)(A)–(G)).
- By specifying these areas, the legislature channels AI R&D toward place-based priorities, potentially accelerating innovation in environmental monitoring, ag-tech, telemedicine, energy grid optimization, digital storytelling, rapid prototyping, and endangered-language modeling.
3. Reporting requirements.
- “The institute shall submit a biannual report to the legislature and the governor. The report shall include: (1) A summary of activities and achievements; (2) Financial statements and funding updates; and (3) Recommendations for future initiatives and funding needs” (§ 304A--, subsec. (e)).
- This reporting mandate creates transparency and accountability for how AI research dollars are spent and what outcomes are achieved.
Section C: Deployment & Compliance
1. No direct product-regulation or certification requirement.
- The text contains no clauses on certifying, registering, or auditing AI-powered products before market release.
- Instead, it focuses on “ethical guidelines and policies for the use of artificial intelligence in Hawaii” (§ 304A--, subsec. (b)(4)).
2. Ethical guidelines.
- “(b)(4) Develop ethical guidelines and policies for the use of artificial intelligence in Hawaii.”
- Although the provision tasks the institute with ethics work, it does not specify enforcement mechanisms, permissible standards, or penalties for non-compliance by private AI vendors or state agencies. The term “ethical guidelines” is open-ended and could range from voluntary best practices to formal administrative rules.
Section D: Enforcement & Penalties
1. Absence of enforcement or penalties.
- No section imposes fines, penalties, or licensure suspensions on organizations that fail to adopt the institute’s guidelines or that misuse AI.
- There are likewise no incentives (tax credits, pilot program exemptions) explicitly tied to AI development or deployment.
2. Optional authority to seek funds.
- “Aside from general funding from the legislature, the institute may seek additional funding through federal research grants, partnerships with private sector organizations, and philanthropic contributions” (§ 304A--, subsec. (d)).
- While not an enforcement mechanism, this clause encourages the institute to leverage external resources to achieve its mission.
Section E: Overall Implications
1. Advancing R&D and workforce development.
- By dedicating $4 million over two years and hiring at least 17 FTEs, the bill substantially invests in AI research and teaching capacity. This will likely attract faculty talent, enable new degree programs (§ 304A--, subsec. (b)(2)(A)), and create K–12 outreach (§ 304A--, subsec. (b)(2)(D)).
2. Shaping Hawaii’s AI ecosystem.
- The place-based focus—tying AI to climate resilience, sustainable agriculture, and cultural preservation—positions Hawaii as a living laboratory for applied AI solutions to island-specific challenges.
- The institute’s public-private partnership mandate (§ 304A--, subsec. (b)(3)) may lower barriers for startups and established tech firms to collaborate with UH, potentially spurring local AI entrepreneurship and job creation.
3. Gaps and uncertainties.
- No binding standards or regulatory oversight for AI systems are included, leaving a potential vacuum if harmful AI applications emerge.
- The open definition of “ethical guidelines” may slow the timely issuance of protective policies or enable voluntary rather than mandatory compliance.
In sum, H.B. 546 is narrowly tailored to build Hawai‘i’s AI research capacity and grow its talent pipeline. It does not regulate deployed AI systems, set safety or privacy requirements, or impose penalties. Its main impact will be to accelerate state-sponsored innovation in AI, aligned with Hawaii’s unique cultural, environmental, and economic priorities.
Senate - 59 - Department of the Attorney General; Algorithmic Decision-Making; Algorithmic Discrimination; Artificial Intelligence; Report
Legislation ID: 28180
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. “Algorithmic eligibility determination” ( § -1 )
- Text: “Algorithmic eligibility determination means a determination based in whole or in significant part on an algorithmic process that utilizes machine learning, artificial intelligence, or similar techniques to determine an individual’s eligibility for, or opportunity to access, important life opportunities.”
- Relevance to AI: Explicitly invokes “machine learning, artificial intelligence, or similar techniques.” It covers any AI-based decision-making about credit, employment, housing, insurance, education, or places of public accommodation.
2. “Algorithmic information availability determination” ( § -1 )
- Text: “Algorithmic information availability determination means a determination… based in whole or in significant part on an algorithmic process that utilizes machine learning, artificial intelligence, or similar techniques to determine an individual’s receipt of advertising, marketing, solicitations, or offers for an important life opportunity.”
- AI focus: Targets AI systems that decide who sees which ads or offers.
3. “Covered entity” ( § -1 )
- Text: “Covered entity means any… organization… that either makes algorithmic eligibility determinations or algorithmic information availability determinations, or relies on… determinations supplied by a service provider…”
- Scope: Applies broadly to any organization using or outsourcing AI for those determinations if it meets size or revenue thresholds or is a data broker.
4. “Service provider” ( § -1 )
- Text: “Service provider means any entity that performs algorithmic eligibility determinations or algorithmic information availability determinations on behalf of another entity.”
- Implication: AI vendors and consultants fall under this definition.
Section B: Development & Research
– There are no direct research-funding or data-sharing mandates in this text. The bill does not address AI R&D grants, open-source, or academic collaboration. Its focus is on deployment and oversight of existing AI systems.
Section C: Deployment & Compliance
1. Prohibition on Discrimination ( § -2(a) )
- Text: “A covered entity shall not make an algorithmic eligibility determination or an algorithmic information availability determination on the basis of an individual’s or class of individuals actual or perceived race, color, religion, … in a manner that segregates, discriminates against, or otherwise makes important life opportunities unavailable…”
- Impact: Directly restricts AI models from using protected attributes, implicitly requiring developers to remove or mitigate bias in training data and model design. Startups and vendors must implement fairness constraints.
2. Notice & Disclosure ( § -4(a) )
- Text: “A covered entity shall… develop a notice that explains how the covered entity uses personal information in algorithmic eligibility determinations… including: (A) What personal information… collects, generates, infers, uses, and retains; (B) What sources…; (C) Whether… shared… with any service providers…”
- Effect: AI deployers must document data provenance, inference processes, and third-party AI services. This increases compliance burdens and may slow deployment for smaller entities.
3. Adverse Action Disclosure ( § -4(d) )
- Text: “If a covered entity takes any adverse action… based in whole or in part on the results of an algorithmic eligibility determination, the covered entity shall provide… disclosure that includes: (2) The factors the determination depended on; and (3) An explanation that the individual may submit corrections… and request… a reasoned reevaluation… by a human.”
- Implication: Mandates explainability and human-in-the-loop processes. AI systems must be auditable to surface factor weights or feature importance.
4. Annual Audits & Impact Assessments ( § -5(a)(1)–(6) )
- Text: “A covered entity shall annually audit its algorithmic eligibility determination and algorithmic information availability determination practices to: (1) Determine whether the processing practices discriminate…; (2) Analyze disparate-impact risks…; (3) Create and retain… audit trail…; (4) Conduct annual impact assessments of… existing systems… and prior to implementation, new systems…; (5) Conduct the audits… in consultation with third parties…; (6) Identify and implement reasonable measures to address risks…”
- Effect: Creates a high compliance bar. AI vendors must build robust logging, bias-testing frameworks, and external audits. Researchers may be engaged as third-party auditors.
5. Reporting to Attorney General ( § -5(b) )
- Text: “A covered entity shall annually submit a report… to the department of the attorney general… containing: (1) The types of algorithmic eligibility determinations…; (2) The data and methodologies…; … (7) The frequency, methodology, and results of the impact assessments…”
- Consequence: Proprietary algorithm details (e.g., model architectures, training data) must be disclosed to regulators, raising IP confidentiality concerns for vendors.
Section D: Enforcement & Penalties
1. Civil Penalty for Violations ( § -6(c) )
- Text: “Any covered entity or service provider that violates this chapter shall be liable for a civil penalty of not more than $10,000 for each violation…”
- Impact: Firms face financial risk for non-compliance; may lead to conservative AI adoption or investment in compliance infrastructure.
2. Private Right of Action ( § -6(e) )
- Text: “Any person aggrieved by a violation… may bring a civil action… and the court may award an amount not less than $100 and not greater than $10,000 per violation or actual damages, whichever is greater.”
- Implication: End-users can sue AI deployers; startups and SMEs could be defendants in class actions.
3. Injunctive Relief & Additional Remedies ( § -6(a),(f) )
- Text: Attorney General may seek “temporary or permanent injunction” (a)(1); courts may award “punitive damages; reasonable attorney’s fees and litigation costs; and any other relief” (f).
- Effect: Regulators and plaintiffs have broad enforcement powers; fosters a climate of risk-aversion around AI deployment.
Section E: Overall Implications
• Compliance Costs: Startups and small businesses may struggle with the annual audit, reporting, and disclosure requirements (§ -4, § -5).
• Innovation Impact: The bill’s stringent fairness and transparency mandates could slow AI deployment or favor established vendors with compliance teams.
• Data & IP Concerns: Requiring vendors to reveal methodologies, training datasets, and performance metrics (§ -5(b)(2)–(6)) may conflict with trade-secret protections, dissuading investment in novel AI architectures.
• Accountability & Trust: By imposing human review post-adverse action (§ -4(d)(3)(C)), the bill aims to ensure recourse and enhance public trust in AI decisions.
• Regulatory Oversight: The Attorney General gains significant authority (§ -6), establishing Hawaii as a proactive jurisdiction in AI governance.
Ambiguities Noted:
• “Reasonably designed measures” (§ -3) lacks binding standards—interpretations may vary.
• The scope of “similar techniques” could encompass future non-AI algorithmic methods, potentially over-broad.
• The threshold of “adverse action” (§ -1) may require case-by-case interpretation in new domains (e.g., algorithmic content moderation).
Senate - 639 - Artificial Intelligence; Chatbots; Unfair or Deceptive Practices; Developer; Penalties; Exemptions
Legislation ID: 27266
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. “Artificial intelligence chatbot” or “chatbot” (Section 2, §481B-Definitions)
– Text: “‘Artificial intelligence chatbot’ or ‘chatbot’ means a software application, web interface, or computer program designed to have textual or spoken conversations that uses a generative artificial intelligence system capable of maintaining a conversation with a user in a manner that uses natural language and simulates the way a natural person would behave as a conversational partner.”
– Relevance: This definition explicitly targets AI systems (“generative artificial intelligence system”) whose primary function is human-like conversation. It captures any modality (textual or spoken), and any delivery platform (application, web interface, program).
2. “Consumer” (Section 2, §481B-Definitions)
– Text: “‘Consumer’ means a natural person who, primarily for personal, family, or household purposes, purchase, attempts to purchase, or is solicited to purchase goods or services…”
– Relevance: Establishes the protected party interacting with AI chatbots in commercial transactions.
3. “Class action” / “de facto class action” (Section 2, §481B-Definitions)
– Text: “includes the definition as provided in rule 23 of the Hawaii rules of civil procedure… same meaning as in section 480-1.”
– Relevance: These definitions set the procedural scope for aggregation of consumer claims against AI-using entities.
Section B: Development & Research
– No clauses in this bill impose R&D funding mandates, data-sharing requirements, or reporting obligations on AI developers beyond marketing disclosures.
– Ambiguity: The bill does not regulate training data, algorithmic transparency, or research safety protocols. Research institutions are unaffected unless they “sell, offer for sale, advertise, or make available” a chatbot commercially (Section 2, §481B-Disclosure required(b)).
Section C: Deployment & Compliance
1. Commercial Deployment Disclosure (Section 2, §481B-Disclosure required(a))
– Text: “No corporation, organization, or individual … shall use an artificial intelligence chatbot … without first disclosing … in a clear and conspicuous fashion that the consumer is interacting with a chatbot…”
– Impact: Requires all commercial actors deploying AI chatbots in customer-facing roles (e.g., customer service, sales, marketing) to label the chatbot. This could raise compliance costs (e.g., UI modifications, staff training) but increases transparency to end-users.
2. Developer Marketing Disclosure (Section 2, §481B-Disclosure required(b))
– Text: “No developer of an artificial intelligence chatbot shall sell, offer for sale, advertise, or make available any artificial intelligence chatbot without disclosing … that the chatbot uses artificial intelligence and is capable of mimicking human behavior …”
– Impact: Affects AI vendors at the point of sale or distribution, forcing them to include disclaimers in product documentation, websites, and advertisements. Startups must build these disclosures into marketing collateral, potentially slowing time-to-market.
3. Small Business Exemption (Section 2, §481B-Disclosure required(a), proviso)
– Text: “provided that small businesses that unknowingly utilize artificial intelligence chatbots … shall not be in violation … unless … provided clear and adequate notice … and fails to comply after being afforded a reasonable opportunity to do so.”
– Impact: Eases burden on small operators who adopt third-party chatbot solutions unknowingly, giving regulators discretion to issue warnings before penalties.
Section D: Enforcement & Penalties
1. Private Right of Action (Section 2, §481B-Suits by persons injured(a))
– Text: “Any person who is injured by a violation … may: (1) Sue for damages … no less than $1,000 or threefold damages … whichever sum is greater, and reasonable attorneys fees …; and (2) Bring proceedings to enjoin the unlawful practices … and … reasonable attorneys fees ….”
– Impact: Strong private enforcement incentivizes class actions. Established vendors face potential high-value suits; consumers gain leverage to enforce disclosures.
2. Class Action Limits (Section 2, §481B-Suits by persons injured(b)(3))
– Text: “Damages awarded shall not exceed $10,000,000.”
– Impact: Caps potential liability in aggregate suits, providing predictability for larger vendors.
3. Attorney General Injunctive Authority (Section 2, §481B-Suits by persons injured(d))
– Text: “The attorney general … may file a petition for injunctive relief against any … who violates this part.”
– Impact: Enables state enforcement but does not specify criminal penalties.
4. Civil Penalties (Section 2, §481B-Penalties)
– Text: “Any corporation, organization, developer, or individual found to be in violation of this part shall be subject to a civil penalty of no more than $5,000,000.”
– Impact: Creates a statutory backstop to private suits and complements injunctive relief; maximum penalty is substantial for large vendors.
Section E: Overall Implications
1. Transparency Emphasis
– By mandating clear notification whenever consumers interact with AI chatbots, the bill prioritizes informed consent and guards against deceptive practices.
2. Compliance Costs vs. Consumer Trust
– Startups and established vendors must update interfaces, marketing, and user agreements to include AI disclosures—potentially delaying deployments. However, greater transparency may build consumer trust in AI services.
3. Enforcement Risk
– The availability of statutory damages, attorney fee awards, and high civil penalties creates strong deterrents against nondisclosure but could prompt defensive compliance strategies or relocation of AI services outside Hawaii’s jurisdiction.
4. Gaps in Oversight
– The bill does not address algorithmic bias, data privacy, model safety, or permissible uses beyond conversation. It leaves broader AI governance to future legislation.
5. Regulatory Focus
– By concentrating on consumer deception, the law frames AI transparency as a consumer-protection issue rather than a technical safety or human-rights issue, shaping the state’s AI regulatory narrative accordingly.
Senate - 640 - Artificial Intelligence; Chatbots; Unfair or Deceptive Practices; Penalties
Legislation ID: 28755
Bill URL: View Bill
Sponsors
Senate - 726 - Data and Artificial Intelligence Governance and Decision Intelligence Center; Established; Data Sharing; Appropriation
Legislation ID: 27352
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. “Data and artificial intelligence governance” (Section 2(c))
– Text: “‘Data and artificial intelligence governance’ means creating and implementing policies, standards, and tools to ensure availability, quality, security, privacy protection, and usability of data and artificial intelligence.”
– Analysis: Explicitly frames AI under the same governance regime as data. It signals that any AI system handling state data must comply with uniform policies on security, privacy, quality, and usability.
2. “Decision intelligence” (Section 2(c))
– Text: “‘Decision intelligence’ means the use of machine learning‐enabled and artificial intelligence‐enabled data analytics and predictive models with visualized insights to provide decision recommendations, support operations, and track the impact of decisions.”
– Analysis: Defines the scope of AI use cases the Center will support—specifically machine-learning and AI for analytics and prescriptive insights.
3. “Integrated data and artificial intelligence platform” (Section 2(c))
– Text: “‘Integrated data and artificial intelligence platform’ means a comprehensive data management solution package, including machine learning and artificial intelligence capabilities, that enables agencies to gather, manage, analyze, visualize, and share data, as well as conduct analytics, predictions, and decision intelligence functions…”
– Analysis: Establishes that the state may procure or build an off-the-shelf or custom “platform” combining data management with AI modules—potentially influencing vendor product requirements and state procurement rules.
4. “Open data” (Section 2(c))
– Text: “‘Open data’ means data that can be shared with the public for use and republication without restriction.”
– Analysis: Positions AI datasets and AI training data insofar as they are state-held as candidates for open publication, subject to privacy exceptions in Section 3(a).
Section B: Development & Research
1. Statewide Data Sharing and Platform (Section 2(b)(2), (5))
– Text:
• “(2) Enabling secured and efficient data sharing across state agencies through implementing statewide data sharing tools and platforms to improve interoperability;”
• “(5) Creating and managing a statewide integrated data and artificial intelligence platform to facilitate data sharing to enable analytics and decision recommendations;”
– Impact: Researchers and startups could gain access to richer, interoperable datasets via the Center’s platform. This lowers the barrier to prototyping AI models on government data, fostering local AI innovation.
2. Master Data Management & Citizen Record (Section 2(b)(4))
– Text: “(4) Collaborating with agencies and utilizing master data management technology to create a statewide master citizen record to enable seamless citizen service and improve citizen experience;”
– Impact: A singular citizen record, if made available under proper privacy controls, could become a powerful testbed for citizen-facing AI services. Could raise privacy concerns requiring strict governance.
3. Agency Support Team (Section 2(b)(6))
– Text: “(6) Establishing a team to support agency implementation of data policies, data management, and data analytics to support agencies and decision makers in evidence‐based decision making;”
– Impact: Directly funds technical assistance for AI/ML projects across departments—potentially accelerating proof-of-concept deployments and fostering intra-governmental R&D.
4. Appropriation for AI Positions (Section 4)
– Quotes:
• “$120,000 for one FTE permanent data and artificial intelligence architect position to design, develop, and optimize data and AI models…”
• “$110,000 for one FTE permanent data and artificial intelligence engineer position to build and optimize data platforms…”
• “$120,000 for one FTE permanent data and artificial intelligence scientist position to use statistics algorithms and machine learning models…”
• “$70,000 for one FTE permanent data and artificial intelligence analyst position to collect, interpret, and analyze business data and convert it into actionable insights…”
– Impact: The State is directly hiring AI talent, effectively growing its in-house R&D capacity. This could attract private AI firms seeking partnership or spin-off opportunities while providing stable career paths in public AI work.
Section C: Deployment & Compliance
1. Access Control & Secured Use (Section 2(b)(3))
– Text: “(3) Implementing data and artificial intelligence policies and statewide data and artificial intelligence governance tools to allow agencies to protect data through proper access control for secured use of data and artificial intelligence;”
– Impact: Imposes compliance obligations on any AI system that processes state data—agencies must integrate access control, audit trails, potentially favoring vendors offering compliance-ready solutions.
2. Public Open Data Requirements (Section 3(a))
– Text:
• “Each executive branch department … shall use reasonable efforts to make appropriate and existing data sets maintained by the department electronically available to the public through … the State’s open data portal …”
• Exceptions: new data need not be created; licensed data or proprietary information may remain closed.
– Impact: Encourages—but does not mandate—publication of AI training data and model outputs. The “reasonable efforts” language may be open to interpretation, leading to uneven compliance.
3. Reporting & Oversight (Chief Data Officer) (Section 3(a))
– Text: “… facilitate data sharing across state agencies … and oversee the data and artificial intelligence governance and decision intelligence center pursuant to section 27-___.”
– Impact: Centralized oversight role could lead to standardized AI procurement reviews, model risk assessments, and governance checklists—likely increasing regulatory certainty but adding procedural overhead for vendors.
Section D: Enforcement & Penalties
– The bill contains no explicit enforcement mechanisms, penalties, or incentives (e.g., fines, certifications) tied to AI use.
– Ambiguity: Compliance appears driven by agency cooperation and “reasonable efforts,” raising questions about accountability if agencies fail to adopt the Center’s policies.
Section E: Overall Implications
1. Advancement
– By centralizing AI governance and funding key roles, the State reduces duplication across agencies and creates a sandbox environment for AI pilots. This could attract AI talent and promote public-private collaboration.
2. Restriction
– Lack of enforcement teeth and reliance on “reasonable efforts” may limit the Center’s authority, potentially leaving critical data siloed and hampering AI’s effectiveness.
3. Ecosystem Shaping
– The new positions and budget (~$650K annually) establish a state-level AI hub, likely to become the focal point for startups seeking public data, for vendors needing to comply with state standards, and for researchers looking for stable funding.
– The definitions carve out a broad remit (from master data to predictive models) but do not address liability, bias mitigation, or auditing—areas where future legislation may build.
4. Ambiguities & Next Steps
– “Reasonable efforts” and lack of penalties leave open how the Center will enforce data publication.
– No timelines or service-level agreements for data sharing are specified.
– The platform’s procurement model (build vs. buy) and vendor certification requirements remain undefined, which may delay implementation.
In sum, H.B. 726 lays the administrative and budgeting groundwork for a comprehensive AI governance structure in Hawaii, focusing on data interoperability, platform creation, and in-house expertise but stopping short of prescribing detailed compliance or enforcement mechanisms.
Illinois
House - 1427 - ALGORITHMICS PROHIBITED-RENT
Legislation ID: 183079
Bill URL: View Bill
Sponsors
House - 1594 - EMPLOYMENT&ACCOMODATION-WEIGHT
Legislation ID: 183246
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of the only provisions in HB 1594 that deal explicitly with “artificial intelligence.” The rest of the bill (weight/size discrimination, public accommodations, pregnancy accommodations, etc.) is outside the scope of AI regulation and therefore is not discussed here.
Section A: Definitions & Scope
1. No AI definition. HB 1594 does not define “artificial intelligence,” “AI system,” “algorithm,” or any related term. This omission leaves open questions about whether “artificial intelligence” refers only to machine-learning based decision tools or also to rule-based software, expert systems, biometric scanners, or other automated decision-support systems.
2. No carve-outs or exemptions. Because “artificial intelligence” is not further circumscribed, the prohibition in subsection (L) potentially applies to any software that an employer describes as “AI.”
Section B: Development & Research
• There are no provisions in HB 1594 that direct funding, reporting, or data-sharing for AI research or development.
Section C: Deployment & Compliance
HB 1594 adds a new subdivision (L) to Section 2-102 of the Illinois Human Rights Act, regulating how employers may use AI in employment decisions:
“(L) Use of artificial intelligence.
(1) With respect to recruitment, hiring, promotion, renewal of employment,
selection for training or apprenticeship, discharge, discipline, tenure,
or the terms, privileges, or conditions of employment, for an employer to
use artificial intelligence that has the effect of subjecting employees
to discrimination on the basis of protected classes under this Article or
to use zip codes as a proxy for protected classes under this Article.
(2) For an employer to fail to provide notice to an employee that the
employer is using artificial intelligence for the purposes described in
paragraph (1).
The Department shall adopt any rules necessary for the implementation and
enforcement of this subdivision, including, but not limited to, rules on the
circumstances and conditions that require notice, the time period for
providing notice, and the means for providing notice.”
(HB 1594, p. 20, lines 14–27 & p. 21, lines 1–7)
Key compliance obligations:
– Employers may not use AI tools if they “have the effect of subjecting employees to discrimination on the basis of protected classes,” or if they use zip codes as a proxy for race, national origin, religion, etc. (Para. (L)(1)).
– Employers must give each affected employee notice that AI is being used for hiring, promotion, discipline, etc. (Para. (L)(2)).
– The Illinois Department of Human Rights must promulgate implementing rules on when and how notice must be given.
Section D: Enforcement & Penalties
• Enforcement will flow through the existing Illinois Human Rights Act process of filing charges with the Department of Human Rights and administrative hearings. No brand-new penalty structure is created. Violations of subsection (L) simply become additional “civil rights violations” under Section 2-102, subject to the Act’s standard remedies (reinstatement, back pay, damages, injunctive relief, civil penalties up to $50,000 per offense and up to $100,000 for willful violations).
• The Department’s rulemaking authority under para. (L) includes setting notice deadlines and formats, but does not by itself add fines or criminal penalties beyond the HRA’s existing framework.
Section E: Overall Implications
1. Restricted deployment of “black-box” AI systems. By barring any AI that “has the effect of subjecting employees to discrimination … on the basis of protected classes,” employers will need to audit or certify their tools for disparate impact, likely slowing or limiting adoption of third-party AI hiring platforms.
2. Notice requirement. Employers must inform every worker in advance that AI is involved in decision-making (e.g., ranking resumes, scheduling interviews, scoring performance). This transparency mandate is novel in Illinois and may force new disclosures or changes in vendor contracts.
3. Ambiguity around “artificial intelligence.” With no statutory definition, employers and vendors may litigate over what tools qualify—does an Excel-based scoring template count? The Department’s forthcoming rules must clarify scope.
4. Enforcement through the existing HRA process. No dedicated AI enforcement office is created; aggrieved employees will file discrimination charges under the Human Rights Act just as they do for bias in traditional interviews or performance reviews.
5. Impact on stakeholders:
– Employers and vendors will incur compliance costs (audits, legal review, notice systems).
– Start-ups offering AI-based HR tools may face market barriers unless they can demonstrate bias-testing and notice procedures.
– Employees gain transparency rights and an anti-discrimination backstop against opaque AI.
– Regulators (IDHR) must develop technical expertise to write rules and adjudicate AI-related claims.
In sum, HB 1594’s AI section (2-102(L)) does not promote or fund research, nor does it restrict data sharing. It principally inserts non-discrimination guardrails into the deployment phase of AI in employment, combined with a duty to notify employees. The biggest open question is how broadly “artificial intelligence” will be interpreted once the Department issues implementing rules.
House - 1806 - THERAPY RESOURCES OVERSIGHT
Legislation ID: 183458
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused reading of HB1806, organized in the requested five sections. All quotations cite section and line numbers from the enrolled bill.
Section A: Definitions & Scope
1. “Artificial intelligence” (Section 10, lines 2–4)
• Quotation: “ ‘Artificial intelligence’ has the meaning given to that term in Section 2-101 of the Illinois Human Rights Act.”
• Analysis: By importing an existing statutory definition rather than defining AI anew, the bill ties its AI rules to a pre-existing legal framework (the Human Rights Act). That definition likely encompasses “machine learning,” “natural language processing,” etc., but the reference is opaque unless one cross-references the Human Rights Act.
2. “Permitted use of artificial intelligence” (Section 15(a), lines 7–12)
• Quotation: “ ‘Permitted use of artificial intelligence’ means the use of artificial intelligence tools or systems by a licensed professional to assist in providing administrative support or supplementary support…where the licensed professional maintains full responsibility…”
• Analysis: This carve-out defines exactly when AI may be used by therapists—only for non-therapeutic tasks, and only under direct professional oversight.
3. Therapeutic vs. non-therapeutic functions (Section 10)
• The bill repeatedly distinguishes “administrative support,” “supplementary support,” and “therapeutic communication.” By excluding AI from “therapeutic communication” (Section 10, lines 11–26), the text implicitly prohibits AI from any direct client treatment.
Section B: Development & Research
HB1806 contains no provisions that fund, mandate, or report on AI research and development. It makes no mention of data-sharing, grant programs, or R&D transparency. Its entire focus is on clinical deployment of AI in therapy settings.
Section C: Deployment & Compliance
1. Use Restrictions (Section 20(a)–(b), lines 3–19)
• Quotation: “An individual, corporation, or entity may not provide, advertise, or otherwise offer therapy or psychotherapy services…unless the therapy…services are conducted by an individual who is a licensed professional.” (20(a), lines 3–8)
• Quotation: “A licensed professional may use artificial intelligence only to the extent the use meets the requirements of Section 15. A licensed professional may not allow artificial intelligence to do any of the following: (1) make independent therapeutic decisions; (2) directly interact with clients…(4) detect emotions or mental states.” (20(b), lines 10–19)
• Analysis: These provisions effectively ban any AI system from autonomous or semi-autonomous therapy. Even emotion-detection algorithms are prohibited.
2. Informed Consent for Recorded Sessions (Section 15(b), lines 15–24)
• Quotation: “No licensed professional shall be permitted to use artificial intelligence to assist in providing supplementary support…where the client’s therapeutic session is recorded or transcribed unless: (1) the patient…is informed in writing…that artificial intelligence will be used…and (2) the patient…provides consent…”
• Analysis: This requirement inserts a procedural hurdle for AI-assisted note-taking or analytics—it demands written, specific opt-in, revocable consent.
Section D: Enforcement & Penalties
1. Civil Penalties (Section 30(a), lines 3–13)
• Quotation: “Any individual, corporation, or entity found in violation of this Act shall pay a civil penalty…not to exceed $10,000 per violation…assessed by the Department after a hearing…pay the civil penalty within 60 days…”
• Analysis: Violations—such as offering AI-only therapy or failing to obtain written consent for recording—carry steep fines. The Department of Financial and Professional Regulation is empowered to investigate and adjudicate.
2. Investigative Authority (Section 30(b), lines 17–19)
• Quotation: “The Department shall have authority to investigate any actual, alleged, or suspected violation of this Act.”
• Analysis: Broad investigatory power means both licensed professionals and AI vendors could be subject to compliance audits.
Section E: Overall Implications
1. Restrictive stance on clinical AI. By outlawing autonomous AI therapy and even banning emotion-recognition tools (20(b)(4)), the bill sharply limits how startups or established vendors can deploy AI in mental-health contexts.
2. Elevated compliance burden. Licensed providers must track AI use, secure explicit written consent for recordings, and supervise all AI outputs. That may deter small practices from adopting AI-driven efficiencies.
3. Limited innovation incentives. The absence of research-oriented provisions (grants, pilot programs, data-sharing) offers no positive impetus for AI development in the state’s behavioral health sector.
4. Consumer protection emphasis. The underlying policy objective is to safeguard patients from unvalidated or unlicensed AI therapy—a reaction to “unregulated artificial intelligence systems” (Section 5, lines 10–12).
5. Regulatory clarity vs. ambiguity. While the bill clearly bars certain AI uses, it relies on an external definition of “artificial intelligence” and does not specify, for example, whether an AI-powered scheduling chatbot falls under “administrative support” (likely yes) or whether emotion-analysis APIs used off-site constitute “detect emotions” (likely yes). Vendors and practitioners will need regulatory guidance to resolve these ambiguities.
House - 1859 - COM COL-COURSE INSTRUCTOR-AI
Legislation ID: 183511
Bill URL: View Bill
Sponsors
House - 2503 - SCH CD-ARTIFICIAL INTELLIGENCE
Legislation ID: 184155
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a structured analysis of HB 2503’s AI-related provisions. Every claim is anchored to the exact bill language.
Section A: Definitions & Scope
1. No standalone “definition” section for AI—“artificial intelligence” is used throughout without formal statutory definition.
• Implicit scope: any “education technologies, including, but not limited to, artificial intelligence technologies.” (Sec. 2-3.118a(a), lines 11–15)
• The term “artificial intelligence tools and applications” appears repeatedly but is never explicitly defined. Possible interpretation: any software or service that uses AI techniques (e.g., machine learning, NLP, computer vision). The lack of definition creates ambiguity about covered systems: Does rule-based tutoring software count?
2. Internet safety curriculum explicitly includes AI:
• “Safe and responsible use of … artificial intelligence, and other means of communication on the Internet.” (Sec. 27-13.3(c)(1), lines 11–13)
• “false representations of individuals created by artificial intelligence” (Sec. 27-13.3(c)(5), lines 22–24)
Section B: Development & Research
HB 2503 contains no direct mandates for AI R&D funding or data-sharing. However, it does impose reporting and evaluative duties that could affect district and vendor research:
1. Annual District Reporting:
• Districts must report “how students, teachers, and district employees use artificial intelligence.” (Sec. 10-20.74, lines 24–27)
— Potential impact: Creates a new dataset on AI usage patterns, which researchers and vendors might mine if publicly available. Districts may need to survey or instrument AI tools usage, potentially requiring deployment of analytics.
2. Advisory Board Expertise:
• Adds “2 experts on educational applications of artificial intelligence” to a 17-member board. (Sec. 2-3.118a(b)(9), lines 15–17)
— Could foster closer collaboration between State Board and AI researchers, possibly influencing state research priorities.
Section C: Deployment & Compliance
This is the core of the bill’s AI-related regulation. It tasks the State Board—and through it, districts and vendors—with standards, evaluation, guidance, and training:
1. Standards for Safety, Transparency, Data Privacy, Educational Quality
• “develop standards concerning safety, transparency, data privacy, and educational quality for any artificial intelligence technology” (Sec. 2-3.118a(c), lines 22–25)
— Impact on vendors: Must design AI tools to meet these undefined state standards before adoption. Ambiguity: No criteria are spelled out yet; rulemaking needed to fill the gap.
2. Evaluation Rubric & Annual Tool Assessments
• “adopt rules … to develop and use a rubric or other method that may be used to evaluate artificial intelligence tools and applications against these standards.” (Sec. 2-3.118a(c)(1), lines 1–4)
• “No later than December 31, 2025 and … July 1 of each subsequent calendar year, the State Board … identify and evaluate the artificial intelligence tools and applications most commonly used in schools.” (Sec. 2-3.118a(c)(2), lines 6–11)
• Publish “a list of technology tools employing artificial intelligence that have been evaluated … and the results of that evaluation.” (Sec. 2-3.118a(c)(3), lines 13–19)
— Impact on deployment: Vendors may seek State evaluation to appear on the list. Districts may consult the list but are not required to follow it (“may not be used to prohibit or require the use of any artificial intelligence technology”). (Sec. 2-3.118a(c)(3), lines 21–24)
— End-users gain transparency into tool compliance; vendors get informal certification though no enforcement or liability safe harbor is provided.
3. Guidance & Professional Development
• State Board, with Advisory Board, must develop guidance on “use of artificial intelligence in education and the development of artificial intelligence literacy.” (Sec. 2-3.118a(d), lines 25–27)
• Guidance topics include: core AI concepts; enhancing teaching and learning; evaluating bias, privacy, transparency; best practices for selection, implementation, evaluation; AI literacy for students; accessibility for all students. (Sec. 2-3.118a(d)(1)–(6), lines 1–13)
• Publish guidance annually by Dec 31, 2025 and July 1 each year; offer synchronous and asynchronous professional development; update at least yearly. (Sec. 2-3.118a(e), lines 1–13)
— Researchers and vendors might align their products and offerings to guidance. Districts and teachers will incur time and possibly cost to complete trainings.
Section D: Enforcement & Penalties
HB 2503 does not specify penalties or enforcement sanctions tied specifically to AI provisions.
1. Rulemaking
• State Board “shall adopt rules necessary” (Sec. 2-3.118a(c)(1), lines 1–2)
— Implied enforcement via administrative rules—compliance may be required for districts to remain eligible for tech grants or to satisfy reporting obligations.
2. State Mandates Act
• Bill cover sheet flags “STATE MANDATES ACT MAY REQUIRE REIMBURSEMENT”
— Districts could seek reimbursement if the new requirements (reporting AI use, training educators) exceed existing state-mandated services.
Section E: Overall Implications
1. Advancement of Safe AI Use
• Creates a centralized advisory mechanism and develops standards/guidance, likely raising baseline knowledge and safety across districts.
2. Ambiguities & Potential Burdens
• “Artificial intelligence technologies” is undefined; districts and vendors may face uncertainty about which tools are subject to which rules.
• No direct funding is provided for compliance (training, reporting, evaluation), shifting costs to districts unless reimbursements materialize under the State Mandates Act.
3. Influence on Ecosystem
• Vendors: Are incentivized to pursue State evaluation to be on the “informational resource” list. Must monitor forthcoming rules and rubrics.
• Researchers & Startups: Opportunities to serve on the Advisory Board, develop PD content, or assist with rubric creation.
• Educators & Students: Will gain AI literacy curricula and training but must contend with new reporting and possible technology assessments.
• Regulators: The State Board must rapidly build administrative capacity (rule-writing, evaluations, guidance updates) by end of 2025.
By anchoring every analysis point to specific bill text, we see that HB 2503 establishes procedural and advisory frameworks for educational AI, but leaves substantial detail—definitions, standards, enforcement—still to be defined in rules and guidance.
House - 3021 - CONSUMER FRAUD-AI DECEPTION
Legislation ID: 184673
Bill URL: View Bill
Sponsors
House - 35 - AI USE IN HEALTH INSURANCE ACT
Legislation ID: 156602
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an analysis of HB0035 (the “Artificial Intelligence Systems Use in Health Insurance Act”), organized as you requested. Every point is anchored to the bill text; where language is unclear, I note possible readings.
Section A: Definitions & Scope
1. “AI system” (Sec. 5, lines 12–17)
Quote: “AI system means a machine-based system that can, with varying levels of autonomy, for a given set of objectives, generate outputs such as predictions, recommendations, content … or other outputs influencing decisions made in real or virtual environments.”
Analysis: This is the central definition. It explicitly covers software that produces “predictions, recommendations, content,” etc., thereby targeting general-purpose AI tools, ML models, expert systems, even generative AI. The phrase “varying levels of autonomy” implies both fully automated and human-in-the-loop systems.
2. “Machine learning” (Sec. 5, lines 25–27)
Quote: “Machine learning means a field within artificial intelligence that focuses on the ability of computers to learn from provided data without being explicitly programmed.”
Analysis: This definition aligns with standard usage; it ensures that any ML-based underwriting or claims-processing falls under the Act.
3. “Predictive model” (Sec. 5, lines 2–5)
Quote: “Predictive model means the processing of historic data using algorithms or machine learning to identify patterns and predict outcomes that can be used to make decisions or support decision-making.”
Analysis: Captures any data-driven scoring or classification model used for risk assessment or claim adjudication.
4. “AI systems program” (Sec. 5, lines 18–22)
Quote: “AI systems program means the health insurance issuer’s controls and processes for the responsible use of AI systems, including governance, risk management, and internal audit functions…”
Analysis: Establishes that each insurer must maintain an internal governance framework specifically for AI.
Scope statement (Sec. 1): “This Act may be cited as the Artificial Intelligence Systems Use in Health Insurance Act.”
Analysis: Confirms the Act’s singular focus on AI in the context of health insurance regulation.
Section B: Development & Research
– There are no provisions in this bill mandating AI R&D funding, university-industry collaboration, or data-sharing for research. Instead, the Act addresses only regulatory oversight and controls around production/deployment of AI by insurers.
Section C: Deployment & Compliance
1. Regulatory oversight (Sec. 10(a), lines 12–19)
Quote: “The Department’s regulatory oversight … includes oversight of the use of AI systems or predictive models to make or support adverse consumer outcomes. … The Department may request information … and a health insurance issuer … must comply.”
Analysis: The Department of Insurance gains explicit investigatory authority over any AI use that leads to adverse decisions (e.g., coverage denials). This imposes ongoing compliance obligations on insurers to document and disclose model details.
2. Prohibition on sole-AI adverse decisions (Sec. 10(b), lines 11–17)
Quote: “A health insurance issuer … shall not issue an adverse consumer outcome … that result[s] solely from the use or application of any AI system or predictive model. Any decision-making process … shall be meaningfully reviewed … by an individual with authority to override the AI systems.”
Analysis: Requires human review of any negative insurance decision initially recommended by an AI. This restricts “fully automated” claim denials, effectively mandating human-in-the-loop. “Meaningfully reviewed” is somewhat vague—could be anything from cursory sign-off to detailed audit.
3. Clinical peer requirement (Sec. 10(b), lines 21–25)
Quote: “When an adverse consumer outcome is an adverse determination regulated under the Managed Care Reform and Patient Rights Act, the individual … shall be a clinical peer as required and defined under that Act.”
Analysis: For medical necessity denials, the overrider must be a licensed clinician. This raises staffing costs for payers.
4. Disclosure rules (Sec. 15, lines 2–9)
Quote: “The Department … may adopt rules … for the full and fair disclosure of a health insurance issuer’s use of AI systems that may impact consumers … notice before the use of AI systems, notice after an adverse decision, … process for correcting inaccurate information, and instructions for appealing decisions.”
Analysis: Empowers the Department to require consumer-facing disclosures about AI usage, data sources, appeal rights. Could slow time-to-market if disclosures must be pre-approved.
5. Compliance program (Sec. 20(b), lines 20–27)
Quote: “The health insurance issuer’s AI systems program shall include policies and procedures … by all employees, directors, … and persons directly or indirectly contracted … The issuer shall be responsible for any noncompliance under this Act.”
Analysis: Codifies that insurers must build AI governance into their vendor and internal-use management. Vendors (third parties) will face audit requests too.
Section D: Enforcement & Penalties
1. Investigation authority (Sec. 10(a), lines 19–24)
Quote: “… The Department’s inquiries may include … requests for information relating to AI systems governance, risk management, … diligence, monitoring, and auditing of data or AI systems developed or used by a third party …”
Analysis: Grants broad subpoena-like authority. Failure to comply could trigger market conduct actions or fines under the Insurance Code (though this bill does not specify new monetary penalties).
2. Tying into existing remedies (Sec. 20(a), lines 12–19)
Quote: “… must comply with all applicable insurance laws and regulations, including laws addressing unfair trade practices and unfair discrimination.”
Analysis: Enforcement will leverage existing statutes against unfair discrimination. If AI systems produce biased outcomes, insurers may face penalties under current law.
Section E: Overall Implications
– Increased Compliance Costs: Insurers must build or expand AI governance programs (Sec. 5: “AI systems program”), hire or assign clinical peers to review determinations (Sec. 10(b)), and respond to detailed information requests (Sec. 10(a)).
– Slower Deployment of Automated Tools: The prohibition against solely AI-based adverse decisions (Sec. 10(b)) effectively prevents fully automated claim denials, limiting efficiency gains from automation.
– Transparency & Consumer Rights: Potential rulemaking (Sec. 15) will require consumer notices and appeal instructions, empowering beneficiaries but adding regulatory burden.
– Market Impact: Startups supplying AI for underwriting or claims must prepare for insurer audits and integration into the insurers’ AI systems program. Vendors will need robust documentation, risk-management materials, and possibly independent audit reports.
– Regulatory Oversight: The Department of Insurance becomes a powerful gatekeeper for AI in health insurance, bridging technical oversight (model details, data sources) and consumer protection (bias, discrimination).
Ambiguities & Interpretations
– “Meaningfully reviewed” (Sec. 10(b)): No standard is given. Could range from signature requirement to quantitative performance checks.
– Enforcement penalties: The Act refers to existing Insurance Code remedies but does not create new fines. The Department’s actual leverage will depend on how aggressively it uses market conduct exams or unfair-trade-practices actions.
House - 3529 - AI PRINCIPLES
Legislation ID: 185181
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a structured analysis of HB 3529, citing the bill text for each point.
Section A: Definitions & Scope
1. “Artificial intelligence system” or “AI system”
• Text: “'Artificial intelligence system' or 'AI system' means a system … that uses methods like machine learning or rules based on logic and knowledge, and that creates and outputs content, predictions, recommendations, or decisions…” (Section 10, lines 13–20)
• Analysis: This definition explicitly targets any automated system with some degree of autonomy, trained on data, and producing outputs affecting people. It is broad enough to cover classical ML, deep learning, expert systems, and similar AI-powered products.
2. “Business”
• Text: “‘Business’ means a person engaged in commercial, industrial, or professional activities. ‘Business’ includes a for-profit entity and a non-profit organization.” (Section 10, lines 21–24)
• Analysis: By defining “business” to include nonprofits, the bill sweeps in almost any organization deploying AI in Illinois, so long as they meet the employee‐count threshold (see next point).
3. Applicability threshold
• Text: “This Act applies to all businesses with 10 or more employees.” (Section 25, lines 1–2)
• Analysis: Small outfits (fewer than ten employees) are exempt. Startups and very small research groups may avoid compliance costs until they scale past that threshold.
Section B: Development & Research
– The bill contains no direct provisions mandating or funding AI research, nor does it require data‐sharing for research. All development‐related obligations flow indirectly from compliance with governance principles (Section 15) and public disclosure (Section 20).
– Ambiguity: “significant changes … established by the Department” (Section 20(a)(1), lines 3–8) could encompass research‐stage prototypes, depending on how “operational contexts” get defined in rulemaking.
Section C: Deployment & Compliance
1. Five Principles of AI Governance
• Text (Section 15, lines 3–19):
(1) Safety: Ensuring systems operate without causing harm…
(2) Transparency: Providing clear and understandable explanations…
(3) Accountability: Identifying and holding individuals or companies responsible…
(4) Fairness: Preventing and mitigating bias…
(5) Contestability: Allowing individuals to challenge and seek redress…
• Analysis: Any covered business must align its AI systems with these five principles. That will likely require internal audits, bias‐testing protocols, and procedures for end‐user appeals.
2. Public Disclosure Requirement
• Text (Section 20(a), lines 1–19): Businesses must publish an annual (and event-driven) “report explaining compliance with the 5 principles,” including design details, training data, risk mitigation strategies, and impact assessments; it must be in “plain language” plus a more detailed layer for experts.
• Analysis: This creates a de facto model card or “AI impact statement” requirement, akin to federal executive‐order recommendations. It will affect product‐release workflows, requiring legal and communications teams to synthesize technical details for public websites.
Section D: Enforcement & Penalties
1. Civil Penalty
• Text (Section 20(b), lines 20–24): “Any business using AI systems shall be subject to a civil penalty of $1,000 for violation of this Act … unless the business (1) properly complies … and (2) publicly discloses compliance ….”
• Analysis: The flat $1,000 fine is modest for large vendors but could be significant for mid-sized businesses. Because it is per‐business (not per day or per violation), companies will likely absorb it as a fixed cost if they fail to comply.
2. Rulemaking Authority
• Text (Sections 15 and 20): “The Department of Innovation and Technology shall adopt rules …”
• Analysis: Much of the Act’s impact hinges on how the Department defines “significant change” or “plain language,” or what testing metrics count under “major decisions made during the design process.” The rulemaking phase will be critical and may involve public comment.
Section E: Overall Implications
– Compliance Overhead: Covered businesses must develop governance frameworks, conduct regular impact assessments, and draft dual‐layer public reports. That will drive demand for compliance tools, consulting services, and in‐house policy roles.
– Barriers for Small Players: By exempting under-10-employee entities, the law delays the burden on nascent startups but may force smaller labs to stay under that threshold or restructure to avoid compliance.
– Transparency Push: Mandated disclosure and contestability rules could bolster public trust and spur standardized AI labeling, but they also risk exposing proprietary information if not carefully scoped in rulemaking.
– Regulatory Precedent: Illinois would join a small set of U.S. jurisdictions imposing substantive governance rules on AI. How strictly the Department enforces the safety, fairness, and accountability principles will signal to other states and potentially influence federal action.
House - 3567 - AI-MEANINGFUL HUMAN REVIEW
Legislation ID: 185219
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. “Automated decision-making system” (Sec. 5, lines 7–15)
• “any software that uses algorithms, computational models, or artificial intelligence techniques … to automate, support, or replace human decision-making.”
• Explicitly includes “machine learning algorithms” and systems “that process data and … generate conclusions, recommendations, outcomes, assumptions, projections, or predictions without meaningful human discretion.”
• Excludes “basic computerized processes” (e.g., calculators, spreadsheets) and “internal management affairs … that do not materially affect the rights, liberties, benefits, safety, or welfare of any individual.”
→ Relevance: This broad definition captures virtually any AI-driven decision support or automation tool used by State agencies, while carving out purely administrative software.
2. “Meaningful human review” (Sec. 5, lines 1–8)
• Review “by one or more individuals who understand the risks, limitations, and functionality of … the automated decision-making system” and who “have the authority to intervene or alter the decision under review.”
→ Relevance: Sets the standard for human–AI collaboration, ensuring that AI outputs never stand alone in decisions affecting individuals.
Section B: Development & Research
– No provisions mandate state funding, research grants, or data-sharing for academic or commercial AI R&D.
– No reporting requirements for pilot AI projects beyond impact assessments (see Section C below).
→ Impact: The bill does not directly promote AI innovation or research; it instead focuses on restricting unreviewed AI usage in government functions.
Section C: Deployment & Compliance
1. Prohibition without human review (Sec. 10(a), lines 1–9)
• “A State agency … shall not utilize or apply any automated decision-making system … without continuous meaningful human review when performing any function that:
(1) is related to the delivery of any public assistance benefit;
(2) will have a material impact on the rights, civil liberties, safety, or welfare of any individual; or
(3) affects any statutorily or constitutionally provided right …”
→ Restricts deployment of AI in high-stakes areas unless a human is continuously overseeing each decision.
2. Procurement restrictions (Sec. 10(b), lines 14–22)
• “A State agency shall not authorize any procurement, purchase, or acquisition of any service or system utilizing … automated decision-making systems … unless such … system is subject to continuous meaningful human review.”
→ Any vendor must design AI tools assuming a human-in-the-loop model; “black box” or fully autonomous systems become ineligible for state contracts in regulated domains.
3. Labor protections (Sec. 10(c), lines 2–23)
• Use of AI “shall not result in the discharge, displacement, or loss of position,” nor “transfer of existing duties and functions currently performed by employees … to an automated decision-making system.”
→ Prevents agencies from replacing staff with AI, maintaining current headcounts and bargaining agreements.
4. Impact assessments (Sec. 15(a)–(b), lines 4–15 & 11–15)
• Agencies must prepare a signed “impact assessment” before deployment and biennially thereafter, including:
– Objectives, algorithm summaries, training data (lines 17–25).
– Testing for “accuracy, fairness, bias, and discrimination,” cybersecurity, privacy, public health/safety risks, misuse scenarios, and data sensitivity (lines 3–18).
– Notification procedures for affected individuals (lines 5–9).
• If assessments “find … discriminatory or biased outcomes,” the agency “shall cease any utilization … of such automated decision-making system” (Sec. 15(b), lines 11–15).
→ Institutes a rigorous auditing and transparency regime; biased or unsafe systems must be paused until remediated.
5. Public disclosure & redactions (Sec. 20(a)–(b), lines 18–25 & 1–10)
• Agencies must submit assessments to the Governor and legislature 30 days before implementation, and publish them online (lines 20–25).
• Sensitive security or privacy details may be redacted with an explanatory statement (lines 1–10).
→ Balances transparency with protection of security-critical or personal data.
Section D: Enforcement & Penalties
– Mandatory cessation: If bias or discrimination is detected, agencies “shall cease any utilization … of such automated decision-making system” (Sec. 15(b), lines 11–15).
– Compliance gatekeeping: Procurement and deployment are effectively infeasible without completing assessments and ensuring human review.
– No explicit monetary fines or criminal penalties are prescribed; noncompliance would likely be challenged via administrative law or injunctive relief.
Section E: Overall Implications
• Advances responsible AI: By mandating human oversight and detailed impact assessments, the bill promotes governance practices aligned with AI ethics.
• Restricts fully autonomous systems: Any AI lacking continuous human-in-the-loop review is barred from critical public-sector uses.
• Affects vendors & startups: To sell to Illinois agencies, AI providers must embed review interfaces and support transparency (algorithmic summaries, bias testing).
• Shields public from discrimination: The compulsory bias audits and “cease use” requirement create a strong deterrent against deploying biased AI in welfare, licensing, or enforcement functions.
• May slow AI adoption: The administrative overhead of biennial assessments, training for reviewers, and ongoing human oversight could deter agencies from piloting AI, or push them toward minimal, low-impact uses.
• Labor stability protected: Employees cannot be displaced by AI, preserving unionized roles but possibly limiting efficiency gains.
House - 3720 - AI-MEANINGFUL HUMAN REVIEW
Legislation ID: 185372
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of HB3720, organized per your requested sections. Every point is tied to in-text citations. Where language is ambiguous, I note possible readings.
Section A: Definitions & Scope
1. “Automated decision-making system”
– The bill defines “automated decision-making system” to mean “any software that uses algorithms, computational models, or artificial intelligence techniques, or a combination thereof, to automate, support, or replace human decision-making.” (Sec. 5, lines 7–10)
– It expressly includes “machine learning algorithms” and “systems that process data… generate conclusions, recommendations, outcomes, … or predictions without meaningful human discretion.” (Sec. 5, lines 11–16)
– It exempts “basic computerized processes” that “do not materially affect the rights… of any individual within the State.” (Sec. 5, lines 17–23)
– Relevance: This core definition targets AI/ML-enabled decision systems and draws a line between trivial automation (e.g., spreadsheets) and higher-risk AI.
2. “Meaningful human review”
– Defined as “review, oversight, and control of the automated decision-making process by one or more individuals who… understand the risks… and who have the authority to intervene or alter the decision… including… to approve, deny, or modify any decision recommended or made by the automated system.” (Sec. 5, lines 1–8 on page 2)
– Relevance: Establishes that AI decisions cannot operate autonomously in certain state functions; human override is mandatory.
3. “State agency” & “Public assistance benefit”
– “State agency” (Sec. 5, lines 9–11) and “public assistance benefit” (Sec. 5, lines 12–22) clarify the institutions and programs covered—i.e., any state-run benefit (cash assistance, unemployment, housing, etc.) using AI must comply.
Section B: Development & Research
• The bill contains no provisions mandating AI research funding, data-sharing for R&D, or partnerships with universities.
• Ambiguity: Does “testing… for accuracy, fairness, bias…” (Sec. 15(a)(4)(A), lines 4–11 on page 6) imply any research collaboration? Likely not; it’s internal validation, not external data-sharing.
Section C: Deployment & Compliance
1. Prohibition without human review
– “A State agency… shall not utilize or apply any automated decision-making system… without continuous meaningful human review when performing any function that:
(1) is related to the delivery of any public assistance benefit;
(2) will have a material impact on the rights… of any individual; or
(3) affects any statutorily or constitutionally provided right… unless the system is subject to continuous meaningful human review.” (Sec. 10(a), lines 1–13 on page 3)
– Impact: Any AI deployment in benefits, licensing, enforcement etc. must include a trained human reviewer at all times.
2. Procurement restrictions
– “A State agency shall not authorize any procurement… of any service or system utilizing… automated decision-making systems… unless such… system is subject to continuous meaningful human review.” (Sec. 10(b), lines 14–24 on page 3)
– Effect on vendors: AI vendors must build in interfaces for human oversight or risk exclusion from state contracts.
3. Impact assessments
– Before deploying AI, agencies must conduct and re-conduct every two years an “impact assessment” signed by those responsible for human review. (Sec. 15(a), lines 4–11 on page 6)
– Required content includes:
• “description of the objectives” (Sec. 15(a)(1), lines 16–18)
• “evaluation of the ability… to achieve its objectives” (15(a)(2), lines 18–20)
• “summary of the underlying algorithms… computational models… design and training data” (Sec. 15(a)(3)(A–B), lines 20–26)
• “testing for accuracy, fairness, bias, and discrimination” with mitigation plans (Sec. 15(a)(4)(A), lines 1–6 on page 7)
• cybersecurity, privacy, safety, foreseeable misuse, data-sensitivity (Sec. 15(a)(4)(B–E), lines 6–18); and
• “notification mechanism… by which individuals… may be notified… of their rights and options.” (Sec. 15(a)(5), lines 23–26)
– Impact on compliance: Agencies must build documentation and testing pipelines; smaller programs may strain resources.
4. Transparency & publication
– Impact assessments must be submitted to the Governor and General Assembly 30 days before deployment (Sec. 20(a), lines 18–22 on page 8) and published online. (Sec. 20(b)(1), lines 23–25)
– Redaction allowed only for narrowly defined security or privacy reasons with an explanatory statement. (Sec. 20(b)(2–3), lines 2–10 on page 9)
– Effect: Creates public accountability; vendors and agencies face public scrutiny of algorithms and data.
Section D: Enforcement & Penalties
• The bill does not specify fines, criminal penalties, or civil actions for non-compliance.
• Implicit enforcement:
– Agencies cannot legally deploy or procure non-compliant systems; any such use could be challenged in court or halted by injunction.
– Sec. 15(b): “if an impact assessment finds… discriminatory or biased outcomes, the State agency shall cease any utilization… of such… system.” (lines 11–15 on page 7)
• Ambiguity: The absence of explicit monetary penalties may limit deterrence; enforcement relies on administrative stoppage and political oversight.
Section E: Overall Implications
1. Restrictive deployment environment—any AI affecting public benefits, rights, welfare, or safety requires ongoing human oversight, extensive testing, and impact assessment.
2. High compliance burden—documenting algorithms, training data, bias mitigation, security and privacy safeguards, and public reporting may slow adoption, especially for small vendors or agencies with limited budgets.
3. Transparency emphasis—public posting of impact assessments promotes accountability but may clash with proprietary protections.
4. Research side-effects—the focus is exclusively on deployment; no R&D support means Illinois may lag in AI innovation compared with states that offer research incentives.
5. Ecosystem reshaping—large vendors with compliance teams and human-in-the-loop products are favored. Startups and open-source projects face higher barriers to enter the state market without built-in oversight features.
In sum, HB3720 is a deployment-focused compliance framework built around mandatory human oversight, thorough impact assessments, and transparency—but lacks explicit enforcement penalties and offers no direct support for AI development or research.
House - 496 - EMPLOYMENT-TECH
Legislation ID: 182148
Bill URL: View Bill
Sponsors
Senate - 1366 - STATE GOVT AI ACT
Legislation ID: 177069
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an analysis of SB1366 (“State Government AI Act”) organized in five sections as requested. All quotations refer to the bill text by section number and, where helpful, line numbers.
Section A: Definitions & Scope
1. “Agency of State government” (Section 5, lines 7–18)
– Quotation: “’Agency of State government’ means: … any agency, department, or office in State government …”
– Analysis: Broadly sweeps in every executive and legislative office. Any AI rule or prohibition will apply statewide.
2. “Artificial intelligence” or “AI” (Section 5, lines 19–22)
– Quotation: “’Artificial intelligence’ or ‘AI’ means technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity, and autonomy.”
– Analysis: This definition is technology-neutral but expansive—it can cover everything from simple machine-learning classifiers to advanced generative models. There is no carve-out for narrow or “weak” AI; all systems purporting to simulate any aspect of human cognition fall under the Act.
Section B: Development & Research
1. Policies & procedures (Section 10, lines 1–7)
– Quotation: “Before January 1, 2028, the Department of Innovation and Technology shall adopt rules establishing policies and procedures concerning the development, procurement, deployment, use, and assessment of artificial intelligence by agencies of State government …”
– Analysis:
• Encourages centralized, government-wide policy development on AI R&D processes.
• While it does not mandate funding or specific R&D projects, it creates a standard-setting role for the Department of Innovation and Technology (DoIT).
• By consulting the Generative AI and NLP Task Force (lines 8–11), DoIT may incorporate best practices and emerging research trends into state policy.
Section C: Deployment & Compliance
1. Prohibition on use without rules (Section 15, lines 12–15)
– Quotation: “Beginning January 1, 2028, unless permitted by rules adopted by the Department of Innovation and Technology, no agency of State government may deploy or use artificial intelligence.”
– Analysis:
• This is a hard stop: state agencies cannot roll out any AI system after 2028 without explicit DoIT approval.
• Potential advance: ensures all state-deployed AI meets baseline standards for safety, bias mitigation, transparency.
• Potential restriction: slows pilot projects or experimental deployments in agencies that lack resources to navigate the new rulemaking process.
• Affects vendors: any company selling AI systems to the state must demonstrate compliance with DoIT’s forthcoming rules to secure contracts.
Section D: Impact Assessment & Reporting
1. Agency reports (Section 20(a), lines 17–22)
– Quotation: “On or before July 1, 2028, and on or before July 1 of every succeeding calendar year, every agency of State government shall submit an impact assessment report on the impact of artificial intelligence on that agency … to the Department of Innovation and Technology.”
– Analysis:
• Requires agencies to inventory and evaluate their AI systems, focusing on public welfare impacts.
• Creates an annual data stream for regulators; can inform future rule revisions.
• Could impose administrative burden, especially on smaller agencies.
2. Department report (Section 20(b), lines 22–24)
– Quotation: “On or before January 1, 2029, and on or before January 1 of every succeeding calendar year, the Department of Innovation and Technology shall submit an impact assessment report … to the General Assembly and the Governor.”
– Analysis:
• Ensures legislative oversight and transparency on statewide AI adoption.
• Helps lawmakers assess whether current rules adequately protect citizens or stifle innovation.
Section E: Enforcement & Penalties
– The Act contains no explicit civil or criminal penalties, fines, or enforcement mechanisms (e.g., Section 15 prohibits deployment without DoIT rules but does not provide a penalty).
– Potential interpretations:
• Non-compliance may result in administrative sanctions such as halting of a project or withdrawal of budget authority.
• The absence of explicit penalties could create ambiguity about how rigorously “permitted by rules” will be enforced. Agencies may interpret the prohibition loosely unless DoIT spells out enforcement in its rules.
Section F: Overall Implications for Illinois’s AI Ecosystem
1. Centralization of Oversight
– DoIT becomes the gatekeeper for all state-level AI activity, from development to deployment (Section 10 & 15).
– Potential benefit: uniform standards, reduced risk of agency-specific lapses.
– Potential downside: bottleneck risk if rule-development or approval processes are slow.
2. Transparency & Accountability
– Annual impact assessments (Section 20) promote ongoing monitoring.
– Legislators and the public receive regular updates, fostering trust but also scrutiny.
3. Innovation vs. Caution
– The prohibition plus rule requirement (Section 15) effectively pauses new AI deployments until rules are in place—likely by end of 2027.
– Startups and vendors aiming at state contracts must factor in compliance costs and lead times.
– Conversely, clear requirements could reduce uncertainties and encourage vendors to build to spec.
4. Ambiguities & Next Steps
– “Policies and procedures” and the scope of “assessment” are undefined—DoIT will need to clarify.
– No criteria for rule approval are spelled out: fairness? explainability? security?
– Implementation guidelines from DoIT will determine whether Illinois becomes a model for responsible government AI use or simply adds red tape.
In sum, SB1366 lays the legal foundation for centralized AI governance in Illinois state agencies. It does not directly fund R&D or impose civil penalties but does create powerful procedural levers—rulemaking authority and mandatory reporting—that will shape both innovation and risk management across the state’s public sector.
Senate - 150 - ELEC CD-AI ADVERT DISCLOSURE
Legislation ID: 175843
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of SB0150, organized by the sections you requested. All citations refer to the introduced bill text.
Section A: Definitions & Scope
1. “Artificial intelligence” (lines 10–19)
• “Artificial intelligence” is defined as “a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments, and that uses machine and human-based inputs to do all of the following: (1) perceive real and virtual environments; (2) abstract such perceptions into models through analysis in an automated manner; and (3) use model inference to formulate options for information or action.”
– Relevance: This definition explicitly targets any AI system that performs perception, modeling, and inference. It is broad enough to cover neural networks, classical machine-learning pipelines, and potentially hybrid human-in-the-loop systems.
2. “Qualified political advertisement” (lines 2–11 of Sec. 9-9.6(b))
• Defined to include “any paid advertisement … relating to a candidate … or a ballot question that contains any image, audio, or video that is generated in whole or substantially with the use of artificial intelligence.”
– Relevance: This clause explicitly extends to any political ad that relies on AI-generated media. It implicitly targets deepfakes, AI voice-overs, synthetic images, and other generative outputs.
Section B: Development & Research
There are no provisions in SB0150 that mandate AI research funding, data sharing, or reporting requirements for development or research institutions. The bill’s sole focus is on disclosures for AI-generated political advertising.
Section C: Deployment & Compliance
1. Disclosure requirement (Sec. 9-9.6(b), lines 12–23)
• “If a person, committee, or other entity … creates, originally publishes, or originally distributes a qualified political advertisement, the qualified political advertisement shall include … a statement that the qualified political advertisement was generated in whole or substantially by artificial intelligence.”
– Compliance rules differ by medium (graphic, audio, video). Example for video: “the statement shall … appear for at least 4 seconds in letters at least as large as the majority of any text … be spoken … and last at least 3 seconds” (lines 3–15 of subsection (b)(3)).
– Impact: All political-ad vendors and campaigns using AI-generated content must update their production workflows to incorporate compliant disclaimers.
2. Exceptions for bona fide news (Sec. 9-9.6(d)(1), lines 1–14)
• Exempts “a radio or television broadcasting station … as part of a bona fide newscast, news interview, news documentary, or on-the-spot coverage … if the broadcast clearly acknowledges … that the … communication generated … by artificial intelligence does not accurately represent the speech or conduct of the depicted individual.”
– Ambiguity: What level of “clear acknowledgement” suffices? The text does not specify font size or script exact wording, nor whether on-screen captions alone meet the requirement.
Section D: Enforcement & Penalties
1. Civil penalties (Sec. 9-9.6(c), lines 16–25)
• “First violation … civil penalty of not more than $250; second or subsequent … not more than $1,000 for each violation.”
• “Each qualified political advertisement … that violates this Section is a separate violation.”
– Impact: For high-volume digital ad buys, a single non-complying campaign could face dozens of violations (and fines). Established ad platforms will need compliance checks to avoid passing illegal content.
2. Safe harbor for platforms (Sec. 9-9.6(e), lines 7–12)
• “A distribution platform is not liable … if it can show that it provided notice to the distributor … of the … prohibitions concerning the failure to disclose content generated … by artificial intelligence.”
– Effect: Platforms like Facebook, Google, and newspapers can avoid liability if they have written policies and notify advertisers. This shifts the onus to platforms to inform ad purchasers.
Section E: Overall Implications
• Transparency mandate: The bill forces disclosure of AI-generated political content, aiming to preserve voter trust and guard against deep-fakes.
• Limited scope: It does not regulate AI research, development, or general commercial AI systems—only political ads.
• Compliance burden: Campaigns, political committees, and digital-ad vendors must adapt asset-production workflows; failure risks escalating fines.
• Platform policies: Online and print platforms will likely mass-distribute written guidelines to advertisers to secure their own safe harbor under Sec. 9-9.6(e).
• Regulatory clarity: The definition of “artificial intelligence” is broad but precise, though some disclosure standards (e.g., what qualifies as “clear and conspicuous”) could generate enforcement debates.
In sum, SB0150 is a narrowly focused transparency law targeting AI-generated political communications. It does not attempt to govern AI development or broader deployments, but rather imposes medium-specific labeling requirements and financial penalties to ensure voters are alerted when political content is synthesized or heavily edited by AI.
Senate - 1556 - SCH CD-ARTIFICIAL INTELLIGENCE
Legislation ID: 177259
Bill URL: View Bill
Sponsors
Senate - 1792 - FRAUD-ARTIFICIAL INTELLIGENCE
Legislation ID: 177495
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a structured analysis of SB1792 as introduced in the 104th Illinois General Assembly. Every claim is tied to a direct citation from the bill text. Where the text is silent or ambiguous, I note potential interpretations.
Section A: Definitions & Scope
1. “Artificial intelligence” (lines 10–15)
• Text: “‘Artificial intelligence’ means a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs… Includes generative artificial intelligence.” (Section 2HHHH(a), lines 10–15)
• Analysis: This broad definition covers any system that “infers… how to generate outputs” and expressly includes “generative artificial intelligence.” It thus reaches both rule-based and learning-based systems, but its focus on “inferr[ing]” suggests a tilt toward statistical/ML-style AI.
• Ambiguity: The phrase “influenc[e] physical or virtual environments” could cover simple recommendation engines as well as robotic systems.
2. “Generative artificial intelligence” (lines 16–21)
• Text: “‘Generative artificial intelligence’ means an automated computing system that, when provided with human prompts… can produce outputs that simulate human-produced content, including… text, images, multimedia… and other content that would otherwise be produced by human means.” (Section 2HHHH(a), lines 16–21)
• Analysis: Explicitly targets LLMs (text), image-generation models, and any future system that generates new content.
3. “Generative artificial intelligence system” (lines 7–10)
• Text: “‘Generative artificial intelligence system’ means any artificial intelligence system whose primary function is to generate content, including, but not limited to, code, text, and images.” (Section 2HHHH(a), lines 7–10)
• Analysis: Narrows the scope to systems designed to produce new content—excluding, for example, AI used solely for data classification or anomaly detection.
Section B: Development & Research
• The bill contains no provisions mandating or funding AI research, no data-sharing requirements, and no academic or industry reporting obligations. Research and development activities are unaffected except to the extent that any prototype UI might later be subject to the warning requirement.
Section C: Deployment & Compliance
1. Warning requirement (lines 12–16)
• Text: “The owner, licensee, or operator of a generative artificial intelligence system shall conspicuously display a warning on the system’s user interface that is reasonably calculated to consistently apprise the user that the outputs of the generative artificial intelligence system may be inaccurate or inappropriate.” (Section 2HHHH(b), lines 12–16)
• Analysis:
– Applicability: Every deployed generative AI application—web apps, chatbots, design tools—must show a warning.
– Scope of actors: “Owner, licensee, or operator” covers in-house developers, third-party deployers, SaaS providers, and potentially OEMs bundling AI features.
– Form of warning: “Conspicuously display” and “reasonably calculated” are vague standards. They could require pop-ups, banners, or disclaimers, but the lack of font-size or placement rules creates compliance uncertainty.
• Potential impact:
– Startups may need to redesign UIs to include warnings.
– Established vendors must audit existing products for compliance.
– End-users gain awareness but may habituate to or ignore repeated disclaimers.
Section D: Enforcement & Penalties
1. Unlawful practice designation (lines 17–18)
• Text: “A violation of this Section constitutes an unlawful practice within the meaning of this Act.” (Section 2HHHH(c), lines 17–18)
• Analysis:
– By folding breaches into the Consumer Fraud and Deceptive Business Practices Act’s enforcement regime, the bill:
* Allows private parties to bring actions (815 ILCS 505/10a) for injunctions, damages, and attorneys’ fees.
* Empowers the Attorney General to seek civil penalties.
– No specific penalty is set for a missing warning; it defaults to statutory ranges under the Act (up to $50,000 per violation, plus treble damages in some cases).
• Ambiguity: It is unclear whether each user interface instance constitutes a separate violation (e.g., a web-based API with millions of users).
Section E: Overall Implications
• Advances transparency: By mandating disclaimers, SB1792 aims to make consumers aware of AI shortcomings (“inaccurate or inappropriate”).
• Limited regulatory reach: The bill does not impose accuracy standards, auditing, explainability, or data-protection requirements—only a user notice.
• Compliance burden: Small developers must add UI elements and assess design, but no complex certification or third-party audit is required.
• Enforcement via existing consumer-protection tools: Private litigants and the Attorney General can enforce, but the lack of detailed guidelines may produce litigation over sufficiency of warnings.
• Innovation impact: Because the bill only adds a disclosure duty and attaches no fines beyond typical consumer-protection remedies, it is unlikely to chill AI R&D but may spur best practices around user UX and risk communication.
In sum, SB1792 narrowly targets generative AI interfaces with a transparency requirement, relying on the Consumer Fraud Act to enforce it. The measure is unlikely to restrain technical innovation but will shape how Illinois-based and serving AI products present themselves to end users.
Senate - 1929 - PROVENANCE DATA REQUIREMENTS
Legislation ID: 177632
Bill URL: View Bill
Sponsors
Senate - 2203 - AUTOMATED DECISION TOOLS
Legislation ID: 177906
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a structured analysis of SB2203 (“Preventing Algorithmic Discrimination Act”) organized into the five sections you requested. Every finding is anchored to exact bill language.
Section A: Definitions & Scope
1. “Artificial intelligence system” (Sec. 5, lines 4–14)
• Text: “ ‘Artificial intelligence system’ means a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs… ‘Generative artificial intelligence system’ means an automated computing system that, when prompted…can produce outputs that simulate human-produced content…”
• Relevance: This broad definition explicitly targets both predictive/classification models (“influenc[e] physical or virtual environments”) and generative models (text, image, multimedia).
2. “Automated decision tool” (Sec. 5, lines 24–27)
• Text: “ ‘Automated decision tool’ means a system or service that uses artificial intelligence and has been specifically developed and marketed to … make, or be a controlling factor in making, consequential decisions.”
• Relevance: This clause carves out AI-powered systems used in high-stakes decisions as subject to the Act.
3. “Consequential decision” (Sec. 5, lines 26–15 of next page)
• Text: “ ‘Consequential decision’ means a decision or judgment that has a legal, material, or similarly significant effect on an individual’s life relating to … employment, education, housing, healthcare, financial services, criminal justice, voting, etc.”
• Relevance: Defines the scope of regulated AI uses by listing domains where algorithmic discrimination is most harmful.
4. “Algorithmic discrimination” (Sec. 5, lines 7–14)
• Text: “…an automated decision tool contributes to unjustified differential treatment or impacts disfavoring people based on race, color, ethnicity, sex…or any other classification protected by State law.”
• Relevance: Centers the Act on bias in AI, explicitly excluding self-testing tools used solely to “identify, mitigate, or prevent discrimination” (lines 15–23).
Section B: Development & Research
Although SB2203 contains no direct R&D funding or data-sharing mandates, several provisions indirectly affect AI development teams:
1. Impact assessments for newly developed tools (Sec. 10(a), lines 5–13)
• Text: “On or before January 1, 2027, and annually thereafter, a deployer…shall perform an impact assessment for any automated decision tool the deployer uses that includes…(1) purpose…(4) analysis of potential adverse impacts on the basis of sex, race…; (5) description of safeguards…; (7) description of how the tool has been or will be evaluated for validity…”
• Impact: R&D teams must design and document bias-mitigation strategies and tool‐validity evaluations up front, adding overhead to product development cycles.
2. Impact assessments for “significant updates” (Sec. 10(b), lines 9–11)
• Text: “A deployer shall … perform, as soon as feasible, an impact assessment with respect to any significant update.”
• Impact: Any major model retraining, algorithmic change, or shift in use case triggers a fresh assessment, possibly slowing iterative research.
3. Exemptions for small developers (Sec. 10(c), lines 11–16)
• Text: “This Section does not apply to a deployer with fewer than 25 employees unless…deployed an automated decision tool that impacted more than 999 people per year.”
• Impact: Eases compliance burden on small startups or academic spin-outs with limited deployment reach.
Section C: Deployment & Compliance
1. Mandatory impact assessments (Sec. 10)
• See above. Every deployer of an AI decision tool in a regulated domain must conduct and document an annual risk assessment.
2. Notification to end-users (Sec. 15(a), lines 17–25)
• Text: “A deployer shall, at or before the time an automated decision tool is used … notify any natural person … that an automated decision tool is being used…provide…(1) purpose; (2) contact information; and (3) plain language description.”
• Impact: Vendors must build UI/UX flows or paper disclosures informing individuals of AI usage—strengthening transparency but adding integration costs.
3. Right to opt out and human alternatives (Sec. 15(b), lines 25–32)
• Text: “If a consequential decision is made solely based on the output … shall accommodate a natural person’s request to not be subject to the automated decision tool and to be subject to an alternative selection process…”
• Impact: Introduces potential parallel manual procedures, raising staffing or process-management costs for service providers.
4. Governance program requirements (Sec. 20(a–b), lines 18–26 on p. 8)
• Text: “A deployer shall establish, document, implement, and maintain a governance program…contain reasonable administrative and technical safeguards to map, measure, manage, and govern … risks of algorithmic discrimination…appropriate to…use case; size, complexity…nature…technical feasibility and cost…”
• Impact: Encourages or compels formal risk management frameworks (e.g., bias audits, logging, staff training, change-management workflows). Likely to favor vendors and consultancies offering compliance tools.
5. Public policy disclosure (Sec. 25, lines 20–27 on p. 10)
• Text: “A deployer shall make publicly available…a clear policy that provides a summary of…(1) types of automated decision tools …; and (2) how the deployer manages the reasonably foreseeable risks of algorithmic discrimination…”
• Impact: Creates reputational incentives and third-party scrutiny; may push vendors to publish white papers or compliance summaries.
Section D: Enforcement & Penalties
1. Private right of action (Sec. 30(b–c), lines 8–18)
• Text: “On and after January 1, 2028, a person may bring a civil action … plaintiff shall have the burden…that … resulted in algorithmic discrimination that caused actual harm…liability for…(1) compensatory damages; (2) declaratory relief; (3) reasonable attorneys fees and costs.”
• Impact: Opens deployers to lawsuits for proven bias harms—insurance, compliance programs, and legal budgets will rise.
2. Attorney General enforcement (Sec. 35(a–b), lines 21–27 on p. 11)
• Text: “Within 60 days after completing an impact assessment … provide … to the Attorney General. A deployer who knowingly violates … liable for … a fine of not more than $10,000 per violation…Each day … gives rise to a distinct violation.”
• Impact: AG can seek daily fines for late or missing assessments; drives rapid compliance but may penalize honest misfires.
3. Unlawful practice under Consumer Fraud Act (Sec. 40, lines 9–14)
• Text: “A violation of this Act constitutes an unlawful practice under the Consumer Fraud and Deceptive Business Practices Act. All remedies, penalties…shall be available…for enforcement.”
• Impact: Reinforces that any breach—failure to notify, assess, govern, or remediate bias—can trigger broad administrative actions, including injunctions and restitution.
Section E: Overall Implications for Illinois’s AI Ecosystem
• Transparency & Accountability Emphasis: By mandating impact assessments, user notices, and public policies, the bill pushes vendors toward explainable and documented AI.
• Compliance Overhead: Annual assessments, governance programs, and notice/opt-out obligations will increase operational costs—favoring larger firms or those with compliance infrastructures.
• Litigation Risk: The private right of action and daily fines create strong deterrents against neglecting bias controls but may chill deployment in marginal use cases.
• Small-Business Carve-Outs: Exemptions for firms under 25 employees or tools impacting fewer than 1,000 persons limit barriers for early-stage startups and academic labs.
• Regulatory Precedent: Illinois becomes the third U.S. state (after NY and CA) to impose process and substantive duties on AI systems, likely prompting similar proposals elsewhere.
• Ambiguities: Terms such as “reasonably foreseeable risks” and “technically feasible” are undefined, giving regulators and courts wide interpretation leeway—which may lead to uneven enforcement.
In sum, SB2203 is squarely focused on AI deployment in high-stakes contexts. It establishes a process-heavy regime of bias impact assessments, user transparency, governance frameworks, and both civil and state enforcement. While it strengthens safeguards against algorithmic discrimination, the compliance burden and litigation exposure are likely to reshape vendor strategies, driving greater investment in fairness tooling and possibly slowing the roll-out of new AI-powered services.
Senate - 2259 - HEALTH CARE GENERATIVE AI USE
Legislation ID: 177962
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a structured, citation-anchored analysis of SB2259’s AI-related provisions.
Section A: Definitions & Scope
1. “Artificial intelligence” (lines 9–14)
• “Artificial intelligence” means a machine-based system…that infers…how to generate outputs such as predictions, content, recommendations, or decisions…” (Sec. 67(a), lines 9–14).
• Relevance: This catch-all definition explicitly includes any “machine-based system” producing human-like outputs and thus sweeps in large-scale neural nets, chatbots, image generators, etc.
2. “Generative artificial intelligence” (lines 5–18)
• “Generative artificial intelligence” means an automated computing system that, when prompted with human prompts…can produce outputs that simulate human-produced content, including, but not limited to…textual outputs…image outputs…multimedia outputs…other content…” (Sec. 67(a), lines 5–18).
• Relevance: This subdefinition zeroes in on AI systems that synthesize new content rather than simply classify, label, or predict.
3. “Patient clinical information” (lines 8–13)
• “Patient clinical information” means information relating to the health status of a patient. “Patient clinical information” does not include administrative matters…” (Sec. 67(a), lines 8–13).
• Relevance: Limits the rule to AI-generated communications about health data, excluding scheduling or billing.
4. Covered Entities (lines 15–26): “health facility,” “clinic,” “physicians office,” “office of a group practice.”
• By defining these settings (Sec. 67(a), lines 15–26), the bill draws a clear boundary around where the AI-disclaimer rules apply.
Section B: Development & Research
SB2259 contains no provisions directly addressing AI R&D, funding, licensing of models, data-sharing mandates, or innovation incentives. There are no reporting requirements for developers, nor any carve-out for academic or open-source research.
Section C: Deployment & Compliance
1. Mandatory Disclaimers (lines 20–27)
• “A health facility, clinic, physicians office, or office of a group practice that uses generative artificial intelligence to generate written or verbal patient communications…shall ensure that the communications include both of the following…” (Sec. 67(b), lines 20–27).
• Relevance: Every generative-AI-produced patient message must carry a prominent disclaimer.
2. Disclaimer Formats (lines 25–40)
• “(1) A disclaimer that indicates…that the communication was generated by generative artificial intelligence and that is provided in the following manner:
(A) …for written communications…disclaimer shall appear prominently at the beginning…
(B) …for continuous online interactions…the disclaimer shall be prominently displayed throughout…
(C) …for audio communications…the disclaimer shall be provided verbally at the start and end…
(D) …for video communications…the disclaimer shall be prominently displayed throughout…” (Sec. 67(b)(1)(A–D), lines 25–40).
• Impact: Developers of telehealth platforms and chatbots must build UI/UX elements or prompts to surface these disclaimers in the prescribed ways.
3. Contact Instructions (lines 11–14 of subdivision (b))
• “(2) Clear instructions describing how a patient may contact a human health care provider…” (Sec. 67(b)(2), lines 11–14).
• Impact: AI deployment must include fail-safe handoff mechanisms. Portal and EHR vendors will need to integrate “call a doctor” buttons or similar features.
4. Human-in-the-loop Exception (lines 15–18)
• “If a communication is generated by generative artificial intelligence and read and reviewed by a human licensed or certified health care provider, the requirements of subdivision (b) do not apply.” (Sec. 67(c), lines 15–18).
• Ambiguity: The term “reviewed” is undefined—does a quick scan suffice? Vendors and providers may interpret “reviewed” variably, leading to compliance uncertainty.
Section D: Enforcement & Penalties
1. Clinics & Health Facilities (lines 19–21)
• “A violation of this Section by a licensed health facility or a licensed clinic is subject to penalties as implemented by the Department by rule.” (Sec. 67(d), lines 19–21).
• Impact: The Department of Financial and Professional Regulation (IDFPR) will have rule-making authority to set fines or corrective actions.
2. Physicians (lines 22–23)
• “A violation of this Section by a physician is subject to penalties as determined by the Medical Board.” (Sec. 67(e), lines 22–23).
• Impact: Individual physicians risk disciplinary action (reprimand, suspension) under the Illinois State Medical Board.
Section E: Overall Implications
• Advances Transparency: By mandating disclaimers on generative AI communications, SB2259 seeks to increase patient awareness that content is machine-generated.
• Compliance Burden on Vendors & Providers: EHR/telehealth platforms must update interfaces, notification logic, and train staff. Smaller clinics and solo practices may face higher per-unit costs.
• Potential Chilling Effect: Generative AI integration into patient communications may slow if disclaimer-and-handoff requirements are deemed too onerous or raise liability concerns.
• Regulatory Precedent: Establishes a state-level model for “AI labeling” in health care that other jurisdictions may copy.
• Ambiguities to Resolve: Lack of definition for “reviewed by a human” and absence of a safe harbor for minor disclaimer placement errors could lead to uneven enforcement.
In sum, while SB2259 does not regulate AI model development or internal R&D practices, it imposes explicit deployment rules—disclaimers and human-contact instructions—on any health care setting using generative AI for clinical communications. This will reshape how health systems implement AI chatbots, virtual assistants, and automated messaging by requiring user-facing labeling and clear escalation paths.
Maryland
House - 1240 - Health Care Providers and Health Insurance Carriers - Use of Artificial Intelligence in Health Care Decision Making
Legislation ID: 92950
Bill URL: View Bill
Sponsors
House - 1331 - Consumer Protection - Artificial Intelligence
Legislation ID: 93123
Bill URL: View Bill
Sponsors
House - 1391 - Education - Artificial Intelligence - Guidelines and Professional Development
Legislation ID: 93232
Bill URL: View Bill
Sponsors
House - 1407 - Commercial Law - Voice and Visual Likeness - Digital Replication Rights (Nurture Originals, Foster Art, and Keep Entertainment Safe Act - NO FAKES Act)
Legislation ID: 93259
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI‐focused breakdown of HB 1407 (“NO FAKES Act”) organized into the requested sections. Every point is anchored to the bill text; where language is open to more than one reading, I note the ambiguity.
Section A. Definitions & Scope
1. “Digital Replica” (§ 11-1701(b), lines 5–25)
– “A newly created, computer–generated, highly realistic electronic representation…”
• This targets AI because most “newly created” realistic likenesses (voice or image) today come from machine-learning models.
– Excludes “electronic reproduction of a sound recording… when authorized by the copyright holder” (b)(2)
• Carves out ordinary remastering or sampling; aims specifically at AI-driven synthetic creation.
2. “Online service” (§ 11-1701(d), lines 27–39)
– Broadly covers platforms that host user-generated content (social media, app stores).
– Implicitly captures AI-based services that allow users to generate or share AI-created replicas.
3. “Production” (§ 11-1701(e), line 41)
– Defined simply as “the creation of a Digital Replica.”
– Implies that any AI model used to generate a replica is engaged in “production.”
4. “Right Holder” (§ 11-1701(f), lines 42–48)
– Includes individuals and those who’ve acquired rights post-mortem.
– Establishes that consent is the trigger for lawful AI usage of likeness.
Section B. Development & Research
HB 1407 contains no direct AI R&D provisions (e.g., no funding requirements, data sharing, or reporting mandates). Its focus is on downstream commercial use and liability rather than upstream research.
Section C. Deployment & Compliance
1. Exclusive Property Right (§ 11-1702(a), lines 1–8)
– “Each individual or right holder shall have the right to authorize the use of the voice or visual likeness … in a Digital Replica.”
– Advance: creators of AI‐driven replicas must obtain licensing; restricts unlicensed AI applications.
2. License Requirements (§ 11-1703(a), lines 11–21)
– Must be in writing, signed, describe specific intended uses, and (for living persons) no longer than 10 years.
– Startups building AI‐l likeness generators will need robust rights-management processes and legal counsel.
3. Postmortem Rights (§ 11-1702(c)–(d), lines 28–7)
– 10 years after death, renewable by showing “active and authorized public use.”
– Could chill AI initiatives tied to deceased public figures (e.g., virtual concerts) unless executors remain active.
4. Online Service Safe Harbor (§ 11-1706(b)–(d), lines 32–14)
– Mirrors DMCA § 512 safe harbors: platform not liable if it designates an agent and expeditiously removes content upon notice.
– AI platforms hosting user‐made replicas must implement notice-and-takedown workflows to keep immunity.
Section D. Enforcement & Penalties
1. Primary Liability (§ 11-1705(a), lines 22–28)
– “A person shall be liable … if the person: (1) produces a Digital Replica without consent; or (2) publishes … without consent.”
– Potentially subjects AI developers, model licensors, or end-users to litigation if they generate/share unlicensed replicas.
2. Knowledge Standard (§ 11-1705(b), lines 1–11)
– Requires “actual knowledge” or “willful blindness” that content is both a replica and unlicensed.
– Ambiguity: what constitutes sufficient evidence for “actual knowledge” of AI-generation?
3. Remedies (§ 11-1707(e), lines 18–31)
– Statutory damages ranging from $5,000 (individual/unlicensed service) to $25,000 (entity) per work or violation, plus actual damages/profits, injunctive relief, attorneys’ fees, and punitive damages for willfulness.
– A $1 million cap (§ 11-1707(f), lines 14–19) if the online service had an “objectively reasonable belief” content wasn’t a disallowed digital replica.
4. Misrepresentation Penalty (§ 11-1706(f)(2)–(3), lines 28–12)
– False takedown notices carry damages: minimum $5,000 or actual harm plus fees.
– Discourages overbroad or bad-faith notices targeting lawful AI content.
Section E. Overall Implications for the State’s AI Ecosystem
– Advances AI accountability: Platform providers and AI developers will need stricter controls, user agreements, and compliance workflows to verify rights before generating or hosting likenesses.
– Restricts unlicensed generative use: Any AI feature that mimics real voices or faces will require integrated licensing solutions or face statutory damages.
– Chills some creative/archival AI use: Documentary, parody, or educational exemptions exist (§ 11-1705(c)), but unclear boundaries could deter legitimate uses.
– Increases regulatory burdens: Small AI startups may lack resources to manage post-mortem rights registrations, platform agent designations, and defensive litigation.
– Encourages development of rights-management tools: Opportunity for AI vendors to build clearance‐automation, watermarking, or provenance tracking services.
In sum, HB 1407 is squarely aimed at AI-generated likenesses. By defining “Digital Replica” as “computer-generated” likenesses, imposing robust consent and takedown requirements, and prescribing stiff damages, the bill erects both compliance hurdles and business opportunities in Maryland’s AI sector.
House - 1425 - Criminal Law - Identity Fraud - Artificial Intelligence and Deepfake Representations
Legislation ID: 93282
Bill URL: View Bill
Sponsors
House - 376 - Maryland Cybersecurity Council - Membership - Alterations
Legislation ID: 91630
Bill URL: View Bill
Sponsors
House - 525 - Election Law - Influence on a Voters Voting Decision By Use of Fraud - Prohibition
Legislation ID: 91771
Bill URL: View Bill
Sponsors
House - 589 - Artificial Intelligence - Causing Injury or Death - Civil and Criminal Liability
Legislation ID: 91835
Bill URL: View Bill
Sponsors
House - 697 - Health Insurance - Artificial Intelligence, Adverse Decisions, and Grievances - Reporting Requirements
Legislation ID: 91945
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of HOUSE BILL 697, organized per your requested structure.
Section A: Definitions & Scope
1. There is no standalone “Definitions” section in HB 697. However, the bill repeatedly invokes two key concepts:
• “Artificial intelligence or automated decision-making systems” (referred to throughout Section 15-147).
• The set of carriers regulated under Title 15, Subtitle 10A (“grievances and appeals”).
2. Scope Statement (implicit)
• Section 15-147 applies “ON A QUARTERLY BASIS, EACH CARRIER” regulated by Maryland insurance law. It thus covers every health insurer doing business in the State that uses any form of AI or automated decision making.
– Quotation: “ON A QUARTERLY BASIS, EACH CARRIER SHALL SUBMIT TO THE COMMISSIONER A REPORT ON THE CREATION, DEPLOYMENT, AND USE OF ARTIFICIAL INTELLIGENCE OR AUTOMATED DECISION–MAKING SYSTEMS BY THE CARRIER…” (Section 15-147, lines 2–5)
3. Implicit Boundary
• “Artificial intelligence or automated decision-making systems” is not narrowly defined, but by mandating disclosure of “training,” “data sources,” “testing for bias,” etc., the statute implicitly covers any model or software that ingests data and produces coverage decisions or recommendations.
Section B: Development & Research
Although HB 697 does not provide research grants or data-sharing mandates, it does impose a continuous oversight burden that will shape R&D priorities within carriers.
1. Mandatory Quarterly Reporting on AI Lifecycle
– What is reported:
a. “WHEN AND FOR WHAT PURPOSE THE ARTIFICIAL INTELLIGENCE OR AUTOMATED DECISION–MAKING SYSTEM IS BEING USED”
b. “THE PERSON RESPONSIBLE FOR TRAINING…”
c. “THE MAJOR SOURCES OF DATA, EXPERTISE, AND METHODS USED TO TRAIN…”
d. “ADDITIONAL GUIDANCE USED…, INCLUDING OUTCOMES AND HOW THEY ALIGNED WITH HUMAN EXPECTATIONS AND VALUES”
e. “TESTS PERFORMED TO IDENTIFY BIAS… AND THE STEPS TAKEN TO… ADDRESS ANY ISSUES OF BIAS”
– Quotation: Section 15-147, lines 5–21
2. Impact on R&D
• Carriers must designate a named individual (“The person responsible for training…”), potentially creating a new compliance role.
• R&D teams will need robust documentation and possibly external audits to satisfy quarterly bias-testing and alignment reporting.
• Over time, smaller carriers or insurtech startups may find the overhead of quarterly compliance onerous, favoring larger incumbents with existing compliance departments.
Section C: Deployment & Compliance
This bill’s primary operational impact lies in how carriers deploy AI in underwriting, utilization review, claims adjudication, and appeals.
1. Transparency Requirements
– Carriers must disclose each AI system’s deployment contexts and human alignment metrics every quarter.
– Quotation: “WHEN AND FOR WHAT PURPOSE THE ARTIFICIAL INTELLIGENCE OR AUTOMATED DECISION–MAKING SYSTEM IS BEING USED” (Section 15-147(1))
2. Bias Testing and Remediation
– Carriers must report “TESTS PERFORMED TO IDENTIFY BIAS… AND THE STEPS TAKEN TO PROACTIVELY ADDRESS ANY ISSUES OF BIAS,” including new training datasets.
– Quotation: Section 15-147(5), lines 19–21
3. Effect on Commercial AI Vendors
• Insurers will demand extensive transparency and audit rights from their AI vendors to satisfy the quarterly reports. Vendors unwilling or unable to share proprietary model details may be excluded from the Maryland market.
• This could spur the growth of specialized “explainable AI” tool vendors who cater to compliance demands.
Section D: Enforcement & Penalties
1. Reporting to the Insurance Commissioner
– The sole enforcement mechanism is the Commissioner’s annual summary report (Section 15-10A-06(b)(1)). No explicit fines or suspensions are added.
– Quotation: “The Commissioner shall… compile an annual summary report based on the information provided…” (Section 15-10A-06(b)(1))
2. Implicit Compliance Pressure
• Although there are no new penalty provisions, non-reporting or false reporting could trigger existing insurer sanctions under Title 15, Subtitle 1.
• Regular public or legislative queries via the Commissioner’s summary could pressure carriers to maintain high compliance.
Section E: Overall Implications for Maryland’s AI Ecosystem
1. Greater Transparency but Higher Compliance Costs
• Insurers gain visibility into where and how AI is used—and must demonstrate ongoing bias monitoring and human-values alignment.
• The quarterly cadence and depth of required data will raise operational costs, especially for smaller carriers or new entrants.
2. Market Consolidation Pressure
• Carriers with mature governance and audit infrastructures may pull ahead, while niche or startup players may struggle to bear compliance overhead.
3. Incentives for Explainability and Fairness Tooling
• A secondary market for bias-detection, alignment-verification, and AI-governance platforms is likely to emerge.
• Vendors that can supply “explainability as a service” will be in higher demand.
4. Regulatory Precedent
• By creating a sector-specific AI reporting regime, Maryland positions itself to expand similar frameworks into other regulated industries.
5. Areas of Ambiguity
a. “Additional guidance… including outcomes and how they aligned with human expectations and values” — no standard is set for “alignment,” leaving insurers uncertain how to measure or report it.
b. No explicit threshold on what constitutes an “automated decision-making system,” so minimal rule-based systems could arguably trigger reporting, imposing unnecessary burden.
In sum, HB 697 explicitly targets health-insurance carriers’ use of AI by instituting a robust quarterly reporting regime on AI governance, data provenance, bias testing, and human-alignment metrics. Its chief effect will be to raise transparency and compliance overhead, encouraging investment in explainable-AI tools while potentially narrowing market participation to better-capitalized players.
House - 740 - Election Law - Campaign Materials - Disclosure of Use of Synthetic Media
Legislation ID: 91986
Bill URL: View Bill
Sponsors
House - 817 - Residential Leases - Use of Algorithmic Device by Landlord to Determine Rent - Prohibition
Legislation ID: 92065
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused breakdown of House Bill 817, organized in the requested five-part structure. All quotations cite section, subsection, and line or paragraph identifiers exactly as they appear in the bill.
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Section A: Definitions & Scope
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1. “Algorithmic device” (Real Property § 8-220(A)(2))
• “Algorithmic device” is defined as “a device that uses one or more algorithms to perform calculations of data, including data concerning local or statewide rent amounts being charged to tenants by landlords, to advise a landlord on the amount of rent that the landlord may consider charging a tenant.”
• The bill explicitly targets algorithm-based pricing tools—tools commonly powered by AI and machine-learning models that ingest market data and output recommended rents.
• Sub-clause (A)(2)(II) confirms that any “product that incorporates an algorithmic device” is also covered, thus sweeping in cloud-based SAAS and mobile apps.
• Exclusions in (A)(2)(III) carve out low-frequency, aggregated publication by a trade association and products used solely for affordable-housing compliance.
2. “Nonpublic competitor data” (Real Property § 8-220(A)(3))
• Defined as information “not widely available or easily accessible to the public … derived from or otherwise provided … by another person that competes in the same market.”
• Examples include actual rent prices, occupancy rates, lease start/end dates, and “other similar information.”
• This locks in the concern that AI tools trained on proprietary data streams—scraped or licensed—are subject to the rent-pricing ban.
3. “Rent” (Real Property § 8-220(A)(4))
• Broadly encompasses “the total amount of rent, including any concessions and fees, that a residential tenant is required to pay.”
• Signals the bill’s intent to capture any AI recommendation affecting base rent, move-in fees, concession structures, etc.
4. Scope statement (Real Property § 8-220(B))
• Prohibition reads: “A landlord may not employ, use, or rely on … an algorithmic device that uses, incorporates, or was trained with nonpublic competitor data.”
• This is a direct ban on AI-powered dynamic-pricing or rent-optimization platforms that ingest confidential market data.
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Section B: Development & Research
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House Bill 817 contains no explicit provisions directing AI or machine-learning research, data-sharing mandates, funding, or academic partnerships. Its focus is purely on commercial deployment of certain AI tools by landlords.
Ambiguity: None of the statutory text addresses exceptions for university labs, open-source model development, or research in the rental-pricing domain. A researcher building a prototype “algorithmic device” that uses nonpublic competitor data could theoretically be ensnared, though academic research is not the bill’s stated target.
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Section C: Deployment & Compliance
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1. Applicability to commercial solutions (Real Property § 8-220(B))
• “In setting the amount of rent … a landlord may not … rely on … an algorithmic device … trained with nonpublic competitor data.”
• Covers first-party landlords and any third-party “causing another to employ” such a device.
2. Carve-outs for non-AI and public data tools
• Exclusion for “periodic report published not more frequently than once per month by a trade association” (A)(2)(III)1.
• Exclusion for products establishing HUD or local affordable-housing limits (A)(2)(III)2.
3. Unaddressed compliance details
• The bill does not specify documentation or audit-trail requirements to prove a tool is AI-free or trained only on public data.
• Unclear whether post-sale software updates that add new data sources invoke retroactive violations.
Ambiguity: A compliant “algorithmic device” might rely solely on publicly scraped Craigslist data. The bill does not require landlords to demonstrate data provenance or permit regulators to inspect model training logs.
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Section D: Enforcement & Penalties
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1. Unfair trade practice (Commercial Law § 13-301(14)(xliv))
• Any violation of Real Property § 8-220 is declared “an unfair, abusive, or deceptive trade practice with the meaning of Title 13 of the Commercial Law Article.”
2. Remedies under Maryland Consumer Protection Act (Real Property § 8-220(C)(2))
• “Subject to the enforcement and penalty provisions contained in Title 13 of the Commercial Law Article.”
• Those provisions include injunctive relief, civil penalties up to $1,000 per violation (or more in certain circumstances), and attorney’s fees.
3. Prospective-only clause (Section 2)
• “This Act shall be construed to apply only prospectively … to rental agreements executed before the effective date.” (Lines 2–5)
• Landlords need not audit or amend existing leases but must ensure new or renewing leases comply.
Ambiguity: The bill does not define “per violation” granularly—whether each lease is a separate violation or each use of the AI tool counts.
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Section E: Overall Implications
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1. Restrictive impact on AI-driven pricing tools
• Vendors of dynamic-pricing platforms (e.g., “RentOptima AI,” “SmartLease”) will be barred from marketing solutions in Maryland that ingest proprietary competitor data.
• Startups may need to reengineer products to use only publicly available data or obtain explicit data-sharing consents, likely reducing model accuracy.
2. Research dead zone
• Lab prototypes using scraped or scraped-like data sets may hesitate to conduct pilot programs in Maryland, fearing consumer-protection enforcement.
3. Landlord and tenant effects
• Landlords lose access to granular, AI-powered rent-optimization insights. They may revert to manual market surveys or aggregated monthly reports.
• Tenants benefit from a ceiling on algorithmically inflated rents, but may see less fine-tuned concessions or promotion strategies.
4. Enforcement burden
• Maryland Consumer Protection Division must become familiar with AI-model auditing or rely on tenant complaints to detect violations.
• Vendors face uncertain risk of suits and injunctive relief with no technical safe harbor for model design or data-audit compliance.
5. State’s AI ecosystem
• Signals Maryland’s intent to regulate a specific use-case of AI (pricing) ahead of broader AI-law frameworks.
• May chill AI innovation in real-estate technology verticals, but still leaves open AI uses in property management (maintenance scheduling, tenant screening using public data, etc.).
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In sum, House Bill 817 explicitly targets AI-driven rent-pricing tools by defining and banning “algorithmic device[s] … trained with nonpublic competitor data,” imposes enforcement through Maryland’s Consumer Protection Act, but offers limited guidance on proving compliance or auditing AI systems. This will materially reshape the deployment of AI in the state’s multifamily and single‐family rental markets.
House - 820 - Health Insurance - Utilization Review - Use of Artificial Intelligence
Legislation ID: 92066
Bill URL: View Bill
Sponsors
House - 823 - Generative Artificial Intelligence - Training Data Transparency
Legislation ID: 92070
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. “Artificial intelligence” (AI) (3.5-801(c))
– Text: “Artificial intelligence means a machine–based system that: (1) can, for a given set of human–defined objectives, make predictions, recommendations, or decisions …; (2) uses machine and human–based inputs to perceive …; and (3) uses model inference to formulate options for information or action.”
– Analysis: This broad definition covers virtually any automated, model-driven system. By tying “predictions,” “recommendations,” and “decisions” to “model inference,” the bill clearly targets modern machine-learning systems.
2. “Generative artificial intelligence” (3.5-801(d))
– Text: “GENERATIVE ARTIFICIAL INTELLIGENCE means artificial intelligence that can generate derived synthetic content, such as text, images, video, and audio, that emulates the structure and characteristics of the data used to train the artificial intelligence.”
– Analysis: Explicitly narrows the bill’s obligations to “generative” AI—e.g., chatbots, image-synthesis tools. This excludes discriminative AI (classifiers) that do not produce new content.
3. “Developer” and “substantially modifies” (3.5-807(A)(2)–(3))
– Text: “Developer means a person or a unit of State or local government that designs, codes, produces, or substantially modifies a generative artificial intelligence system.” “Substantially modifies means to release a new version … that materially changes the functionality, performance, or training data ….”
– Analysis: By defining “developer” so broadly, the bill captures both commercial vendors and in-house government teams. “Substantially modifies” ensures updates (e.g., retraining on new data) trigger transparency duties.
4. Applicability & carve-outs (3.5-807(B))
– Text: “This section applies to a generative artificial intelligence system that was released on or after January 1, 2022, for use by the general public in the State ….” Exemptions include systems “exclusively made for use by hospital medical staff,” “ensuring physical safety,” “protecting confidential personal information,” “detecting … malicious … actions,” aviation, or federal defense.
– Analysis: The bill covers public-facing gen-AI products but shields certain regulated or safety-critical domains. Ambiguity arises around “exclusively made for use by hospital medical staff”: does a tele-health chatbot fall under exemption?
Section B: Development & Research
– No direct funding mandates or R&D grants.
– Indirect research impact via transparency: by requiring devs to publish training-data details (3.5-807(C)), researchers gain visibility into proprietary corpora and labeling schemes.
• Text: “On or before January 1, 2026 … the developer … shall post on the website … documentation detailing the data and datasets used to train the generative artificial intelligence system, including: (1) the sources or owners of the data; (2) a description of how the data furthers the intended purpose …; (3) size, labels, IP status, personal information usage, cleaning processes, dates collected, and use of synthetic data.” (3.5-807(C)(1)–(12))
– Possible chilling: startups may avoid developing gen-AI or use out-of-state hosting to sidestep web-posting rule.
Section C: Deployment & Compliance
1. Pre-release/post-modification transparency (3.5-807(C))
– Obligation: “before each time … a developer releases or substantially modifies a generative AI system, the developer … shall post …”
– Impact: Enforces a public audit trail of training inputs. Could assist regulators, but creates operational overhead.
2. Public posting mechanism
– Requirement to use “the website of the developer.” No central registry. Ambiguity: what if a dev lacks a public website? No fallback.
3. No third-party audit or certification procedures specified
– The bill stops short of mandating independent audits or proof of data quality/fairness.
Section D: Enforcement & Penalties
– The text contains no express enforcement provisions (e.g., fines or injunctive relief) tied to non-compliance.
– Implicit enforcement via public scrutiny: missing disclosures may harm reputation or trigger lawsuits under consumer-protection statutes.
– Ambiguity: Without a designated enforcing agency or penalty schedule, compliance may be uneven.
Section E: Overall Implications
1. Transparency push
– By compelling gen-AI developers to reveal training-data provenance, the bill promotes accountability, supports fairness research, and may deter the use of biased or illegally scraped datasets.
2. Administrative overhead
– Startups and open-source projects must track data provenance meticulously—even minor updates trigger full re-publication—potentially slowing innovation.
3. Regulatory gap
– No mechanisms for verifying the accuracy of the posted documentation or for penalizing false/misleading disclosures.
4. Market effects
– Large incumbents with legal teams can comply more easily, possibly entrenching their market position. Smaller players may exit the market or host services outside Maryland to avoid obligations.
5. Future expansion
– Coupled with existing definitions of “high-risk AI” (3.5-801(e)), the foundation is laid for more stringent impact assessments (§ 3.5-803) or outcome audits targeting rights-impacting AI in later legislation.
House - 956 - Consumer Protection - Workgroup on Artificial Intelligence Implementation
Legislation ID: 92277
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a structured analysis of House Bill 956 (“Consumer Protection – Workgroup on Artificial Intelligence Implementation”), with every claim tied to quoted passages from the bill text.
Section A: Definitions & Scope
1. No standalone definition of “artificial intelligence” appears in the bill. Instead the terms “artificial intelligence” and “AI” are invoked throughout the workgroup’s mandate, implicitly establishing scope around “AI systems, AI development processes, and AI-powered products.”
• “(A) THERE IS A WORKGROUP ON ARTIFICIAL INTELLIGENCE IMPLEMENTATION.” (Art. – SFP § 3.5-807(A))
• The title itself ties to “Consumer Protection” and “Artificial Intelligence Implementation,” indicating that all aspects of the workgroup’s charge relate back to AI.
2. Scope: By placing the workgroup under the State Finance and Procurement Article, the bill explicitly scopes AI oversight into state contracting, procurement, and finance channels, thereby encompassing both public and private sector uses of AI in the State.
• “BY adding to Article – State Finance and Procurement Section 3.5–807”
Section B: Development & Research
1. No direct R&D funding or data-sharing mandates for AI developers are introduced. The only fiscal provision is an annual appropriation for the workgroup itself:
• “(I) FOR FISCAL YEAR 2027 AND EACH FISCAL YEAR THEREAFTER, THE GOVERNOR MAY INCLUDE IN THE ANNUAL BUDGET BILL AN APPROPRIATION OF $100,000 TO SUPPORT THE WORKGROUP.” (Art. – SFP § 3.5-807(I))
This is not an R&D grant but covers administrative costs, so it does not directly fund AI research.
2. Reporting requirements: The workgroup must study and report on various AI-related topics:
• “(G) ON OR BEFORE JULY 1, 2026, AND EACH YEAR THEREAFTER, THE WORKGROUP SHALL REPORT ITS FINDINGS AND RECOMMENDATIONS, … TO THE SENATE FINANCE COMMITTEE AND THE HOUSE ECONOMIC MATTERS COMMITTEE.” (Art. – SFP § 3.5-807(G))
This requirement can indirectly influence research and development priorities by highlighting areas of need in annual reports.
Section C: Deployment & Compliance
1. Regulation of AI in high-impact decision-making:
• “(F)(1) THE REGULATION OF ARTIFICIAL INTELLIGENCE USED IN DECISIONS THAT SIGNIFICANTLY IMPACT THE LIVELIHOOD AND LIFE OPPORTUNITIES OF INDIVIDUALS IN THE STATE;” (Art. – SFP § 3.5-807(F)(1))
By specifically calling out “decisions that significantly impact … livelihood and life opportunities,” the workgroup’s mandate covers credit scoring, hiring algorithms, insurance underwriting, and other domains.
2. Deployer and developer obligations:
• “(F)(2) DEPLOYER AND DEVELOPER OBLIGATIONS RELATED TO LABOR AND EMPLOYMENT AND PROTECTION OF INDIVIDUAL PRIVACY RIGHTS;” (Art. – SFP § 3.5-807(F)(2))
This clause invites recommendations on transparency, accountability, and privacy for organizations creating or using AI, potentially leading to future compliance regimes for workforce management systems, surveillance tools, or customer-facing bots.
3. Consumer rights protections:
• “(F)(3) PROTECTION OF CONSUMER RIGHTS;” (Art. – SFP § 3.5-807(F)(3))
• “(F)(5) GENERAL ARTIFICIAL INTELLIGENCE DISCLOSURES FOR ALL CONSUMERS;” (Art. – SFP § 3.5-807(F)(5))
The workgroup is charged with studying whether consumers must be informed when they are interacting with or being evaluated by an AI system—potentially leading to labeling requirements or “right to explanation” protocols.
4. Current private-sector uses and public benefit eligibility:
• “(F)(4) CURRENT PRIVATE SECTOR USE OF ARTIFICIAL INTELLIGENCE;” (Art. – SFP § 3.5-807(F)(4))
• “(F)(7) THE IMPACT OF THE USE OF ARTIFICIAL INTELLIGENCE IN THE DETERMINATION OF GOVERNMENT BENEFITS.” (Art. – SFP § 3.5-807(F)(7))
These items extend the workgroup’s lens to both private-sector innovation and essential public-sector determinations—unemployment insurance, Medicaid eligibility, social services—which may spur guidelines on fairness and bias.
Section D: Enforcement & Penalties
1. Enforcement authority study:
• “(F)(6) ENFORCEMENT AUTHORITY FOR THE OFFICE OF THE ATTORNEY GENERAL’S OFFICE OF CONSUMER PROTECTION DIVISION;” (Art. – SFP § 3.5-807(F)(6))
The workgroup must consider whether and how Maryland’s Consumer Protection Division should have explicit enforcement powers over deceptive or harmful AI uses—a precursor to statutory penalties or injunctive relief.
2. No immediate penalties:
The bill itself does not impose fines, criminal sanctions, or civil penalties. Instead, it charges the workgroup with studying enforcement mechanisms and making recommendations in its annual report. Any actual penalties would require follow-on legislation.
Section E: Overall Implications for Maryland’s AI Ecosystem
1. Coordination:
• “(D) IT IS THE INTENT OF THE GENERAL ASSEMBLY THAT THE WORKGROUP SHALL COORDINATE WITH THE MARYLAND CYBERSECURITY COUNCIL …” (Art. – SFP § 3.5-807(D))
This directs integration between AI policy and cybersecurity best practices, potentially leading to joint guidance on secure AI deployment.
2. Diverse stakeholder representation:
The workgroup’s membership spans legislators, executive‐branch officials, industry representatives (tech, biotech, real estate, health care, education), academia, civil liberties groups, veterans’ commerce, nonprofits, cybersecurity experts, and labor organizations (AFL-CIO Tech Institute). This wide net increases the likelihood that final recommendations will balance innovation with consumer protections.
3. Sunset:
• “This Act… shall take effect July 1, 2025. It shall remain effective for a period of 4 years and, at the end of June 30, 2029, this Act… shall be abrogated…”
The sunset date of mid‐2029 means that unless extended, the workgroup—and its momentum—will expire, placing pressure on stakeholders to convert recommendations into permanent law by that deadline.
4. Impact on stakeholders:
– Researchers/Academia: May gain a forum to voice technical concerns and ethical frameworks.
– Startups/Smaller Vendors: Will need to track evolving best practices and potential disclosure obligations.
– Established Vendors: Might face new compliance burdens (audits, transparency, data-handling rules) if the workgroup’s recommendations are enacted.
– End-Users/Consumers: Stand to receive stronger privacy protections, “right to explanation,” and possibly more robust recourse for AI-driven harms.
– Regulators (OAG, Cybersecurity Council): Could see expanded enforcement authority and cross-agency collaboration.
In sum, HB 956 does not itself regulate AI directly but creates a permanent, multi-stakeholder forum mandated to study, report on, and recommend a broad array of AI governance measures—ranging from consumer disclosures to enforcement powers. Its ultimate impact will hinge on the quality of its reports and the speed with which the General Assembly translates them into binding law.
House - 981 - State Department of Education and Department of Information Technology - Evaluation on Artificial Intelligence in Public Schools
Legislation ID: 92347
Bill URL: View Bill
Sponsors
Senate - 1025 - Commercial Law - Voice and Visual Likeness - Digital Replication Rights (Nurture Originals, Foster Art, and Keep Entertainment Safe Act - NO FAKES Act)
Legislation ID: 95172
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused reading of Senate Bill 1025 (“NO FAKES Act”). Each point is tied to exact language in the text, with section and line references.
Section A: Definitions & Scope
1. “Digital Replica” (AI-centric):
• “DIGITAL REPLICA means a newly created, computer-generated, highly realistic electronic representation…” (§11-1701(b), lines 4–7).
– Why AI: “computer-generated” and “highly realistic” target generative AI models (e.g., voice-cloning, deepfake video).
• Exclusions carve out ordinary copyright uses (sampling, remastering) but leave in scope only AI-style recreations (§11-1701(b)(2), lines 17–25).
2. “Production” (of AI replicas):
• Defined simply as “the creation of a digital replica” (§11-1701(e), line 15–16).
– Implicitly covers AI model training and inference that produce replicas.
3. “Online Service”: platform liability and takedown obligations (§11-1701(d), lines 27–35).
• Broadly includes any website or app hosting “user-generated content” or “digital music provider” (17 U.S.C. §115(e)) at lines 28–34.
– Targets social media, streaming platforms, AI marketplaces.
Section B: Development & Research
The bill contains no direct R&D mandates, funding provisions, or data-sharing rules. It focuses entirely on post-production rights and liability.
Section C: Deployment & Compliance
1. Right to authorize AI use:
• “Each individual or right holder shall have the right to authorize the use of the voice or visual likeness … in a digital replica” (§11-1702(a)(1), lines 24–27).
– Effect: All AI developers must secure consent for any voice-cloning or avatar generation.
2. Licensing requirements for AI-created replicas:
• Must be in writing, signed, specify intended uses, and be limited to 10 years (§11-1703(a)(1)–(2), lines 14–20).
• Stricter for minors: licenses ≤ 5 years, court-approved, and terminate at age 18 (§11-1703(b)(1)–(2), lines 22–31).
3. Postmortem rights registry for AI:
• Right holders must file a “notice with the Secretary of State” during a 2-year window to renew post-death rights for their AI clones (§11-1704(a)(1)–(4), lines 1–10).
• Registry public and searchable (§11-1704(b)(1)–(2), lines 11–18).
4. Safe-harbor for AI platforms (“Online Service”):
• No secondary liability if the service removes unauthorized replicas “as soon as is technically and practically feasible” after notice (§11-1706(b), lines 1–8, and (c), lines 1–7).
• Must designate a takedown agent and publish its contact (§11-1706(d)(1)–(2), lines 12–22).
• Secretary of State to maintain the agent directory (§11-1706(e), lines 23–31).
Section D: Enforcement & Penalties
1. Direct liability for unauthorized AI-generated replicas:
• “A person … produces a digital replica without consent … or publishes … without consent” is strictly liable (§11-1705(a)(1)–(2), lines 1–9).
2. Knowledge requirement:
• Must have “actual knowledge or … willfully acted to avoid knowledge” that the work is unauthorized (§11-1705(b)(1)–(2), lines 11–19).
3. Civil remedies per violation:
• Individuals: $5,000 per work; online service entities: $5,000 per violation; other entities: $25,000 per work (§11-1707(e)(1)(I)–(III), lines 18–27).
• Also actual damages, profits, injunctive relief, punitive damages for willfulness, and prevailing-party attorneys’ fees (§11-1707(e)(1)–(4), lines 17–35).
4. Misrepresentation penalties in takedown notices:
• $5,000 minimum damages or actual damages plus costs and fees if someone knowingly misrepresents in a takedown notice (§11-1706(f)(2)–(3), lines 22–35).
5. Limitation for platforms with “objectively reasonable belief”:
• Caps liability at $1 million if the platform reasonably believes the content is non-replica (§11-1707(f), lines 1–6).
Section E: Overall Implications for AI Ecosystem
1. Consent-first mandate: All AI voice-cloning and visual likeness tools must implement workflows to obtain and track rights holders’ written licenses.
2. Compliance burden on startups: rigorous contract and record-keeping functions, registry checks, takedown procedures, and agent designation.
3. Platform risk management: social media and AI marketplaces will need automated detection plus human review to honor notices “as soon as technically and practically feasible.”
4. Potential chilling effect on research: absence of academic or research carve-outs means prototype “deepfake” demos risk liability, possibly stifling open innovation.
5. Clear incentives for robust rights management systems and legal teams; likely drives consolidation around vendors offering “rights-compliance as a service.”
Ambiguities noted:
– “Highly realistic” is undefined, leaving edge-case AI renderings in legal limbo (§11-1701(b), lines 4–7).
– “Technically and practically feasible” removal timeframe is vague (§11-1706(b), lines 1–8), inviting disputes over adequacy of takedown.
Senate - 294 - Maryland Cybersecurity Council - Alterations
Legislation ID: 94090
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of Senate Bill 294. I have organized it into the five requested sections. Because the bill mainly expands the Maryland Cybersecurity Council’s remit to include artificial intelligence (AI) (and quantum computing), there are relatively few “classic” AI-regulatory provisions (e.g., data-sharing mandates, liability rules, or certification regimes). Rather, the bill adds AI to the list of technologies the Council must assess and address.
Section A: Definitions & Scope
1. No formal “Definitions” section for AI. However, the bill explicitly brings AI into scope in two key places:
• Section 9–2901(j), lines 8–11:
“The Council shall work with … to … ASSESS AND ADDRESS CYBERSECURITY THREATS AND ASSOCIATED RISKS FROM ARTIFICIAL INTELLIGENCE AND QUANTUM COMPUTING …”
• Section 9–2901(c)(18), lines 24–30 (higher-ed membership):
“EIGHT NINE representatives from institutions of higher education … WITH EXPERTISE IN CYBERSECURITY, WITH AT LEAST FOUR REPRESENTATIVES WITH EXPERTISE IN ARTIFICIAL INTELLIGENCE AND QUANTUM COMPUTING …”
2. Implicit AI scope:
• By treating AI alongside quantum computing under the Council’s charge, the bill signals that “AI systems, AI development processes, or AI-powered products” are to be viewed principally as sources (and targets) of cybersecurity risk rather than as subjects of industrial policy, data-privacy law, or ethical governance.
Section B: Development & Research
There are no direct provisions in this bill for AI R&D funding, data-sharing mandates, or reporting requirements on AI developers. The Council’s new AI-related research tasks are limited to:
• Risk assessment:
– Section 9–2901(j)(8), lines 2–4:
“ADDRESS EMERGING THREATS POSED BY ARTIFICIAL INTELLIGENCE, INCLUDING: (I) ADVERSARIAL ARTIFICIAL INTELLIGENCE; (II) CYBER ATTACKS; (III) DEEPFAKE TECHNOLOGIES; (IV) UNETHICAL USE; AND (V) FRAUD;”
This clause directs the Council to catalogue and analyze AI-related threat vectors (adversarial ML, deepfakes, fraud, etc.).
Section C: Deployment & Compliance
The bill does not impose prescriptive rules (e.g., certification, audit, or liability) on AI systems themselves. However, it could indirectly affect deployment by:
• Shaping Council recommendations: Section 9–2901(j)(9), lines 7–11:
“recommend any legislative changes considered necessary by the Council to address cybersecurity issues.”
If the Council finds that AI-driven automation or decision-making products present novel cybersecurity risks, it could recommend future deployment restrictions or compliance regimes.
Section D: Enforcement & Penalties
This bill contains no enforcement mechanisms or penalties specific to AI. Its enforcement architecture remains the standard for Council activities: the Council “shall … recommend” but has no direct sanctioning authority.
Section E: Overall Implications
1. Elevates AI as a first-class cybersecurity concern alongside quantum computing. By explicitly tasking the Council to “ASSESS AND ADDRESS CYBERSECURITY THREATS AND ASSOCIATED RISKS FROM ARTIFICIAL INTELLIGENCE” (9–2901(j), lines 8–11), the bill ensures that state-level cybersecurity policymaking will systematically include AI threat modeling.
2. Builds a multidisciplinary Council with AI expertise. Expanding the higher-ed seats to require “AT LEAST FOUR REPRESENTATIVES WITH EXPERTISE IN ARTIFICIAL INTELLIGENCE AND QUANTUM COMPUTING” (9–2901(c)(18), lines 24–30) integrates academic AI research into state cybersecurity strategy.
3. Leaves open detailed regulation for later. Because the Council’s charge is limited to assessment, addressing threats, and making legislative recommendations, any binding AI-specific rulemaking is deferred pending the Council’s future work.
4. Potential impact on stakeholders:
• Researchers and universities may gain influence via Council seats but no direct research funding.
• Start-ups and vendors face no immediate compliance obligations; instead, they may see new best practices or legislative proposals emerge.
• Regulators will have a formal channel (the Council) for AI risk intelligence but will not obtain direct rulemaking power over AI from this bill alone.
Ambiguities & Interpretations
– “ADDRESS … UNETHICAL USE” of AI (9–2901(j)(8)(IV)) is undefined: could be read to include bias, privacy intrusion, or general trustworthiness. Until the Council clarifies, this term covers a broad remit.
– The term “cybersecurity threats … from artificial intelligence” could mean threats posed by AI tools (e.g., automated hacking) or threats to AI systems themselves (e.g., model extraction). The dual interpretation may broaden or narrow the Council’s investigations.
In sum, SB 294 formally incorporates AI (and quantum computing) into Maryland’s statewide cybersecurity governance framework, mandating threat assessments, academic involvement, and future legislative recommendations—but it does not yet create binding AI regulations.
Senate - 609 - Residential Leases - Use of Algorithmic Device by Landlord to Determine Rent - Prohibition
Legislation ID: 94704
Bill URL: View Bill
Sponsors
Senate - 704 - State Department of Education and Department of Information Technology - Evaluation on Artificial Intelligence in Public Schools
Legislation ID: 94853
Bill URL: View Bill
Sponsors
Senate - 905 - Criminal Law – Identity Fraud – Artificial Intelligence and Deepfake Representations
Legislation ID: 95053
Bill URL: View Bill
Sponsors
Senate - 906 - Education - Artificial Intelligence - Guidelines, Professional Development, and Task Force
Legislation ID: 95055
Bill URL: View Bill
Sponsors
Senate - 936 - Consumer Protection - High-Risk Artificial Intelligence - Developer and Deployer Requirements
Legislation ID: 95085
Bill URL: View Bill
Sponsors
Senate - 987 - Artificial Intelligence - Health Software and Health Insurance Decision Making
Legislation ID: 95132
Bill URL: View Bill
Sponsors
Massachusets
House - 1136 - An Act improving the health insurance prior authorization process
Legislation ID: 87141
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. “Artificial intelligence” defined (Sec. 8, new § 12D(a), lines 2–8):
• Text: “For purposes of this subsection, ‘artificial intelligence’ means an engineered or machine-based system that … can … make predictions, recommendations or decisions influencing real or virtual environments.”
• Analysis: This explicit definition anchors all subsequent obligations to “AI,” covering any software that uses “machine-based inputs,” “abstraction,” and “model inference.” It is broad enough to include rule-based expert systems, machine-learning models, and hybrid tools.
2. Scope of regulated AI use (Sec. 8, § 12D(b) introductory clause):
• Text: “A carrier or utilization review organization that uses an artificial intelligence, algorithm or other software tool for the purpose of utilization review or utilization management functions …”
• Analysis: The bill targets AI in the narrow context of insurance utilization review: prior authorization, medical necessity determinations, appeals, etc. It does not extend to general-purpose AI systems outside health insurance.
Section B: Development & Research
No provisions mandate AI R&D, data sharing for innovation, or funding. The bill does not create research grants or require carriers to develop in-house AI; it only regulates existing AI use in utilization management.
Section C: Deployment & Compliance
1. Data inputs and decision basis (Sec. 8, § 12D(b)(1), lines 11–18):
• Text: “the AI … bases determinations on … (i) an enrollee’s medical or other clinical history; (ii) individual clinical circumstances as presented by the requesting provider; (iii) other relevant clinical information contained in the enrollee’s medical or other clinical record.”
• Analysis: Imposes a requirement that AI decisions rely on individualized clinical data, blocking sole use of population-level statistics.
2. Prohibition of group-only datasets (Sec. 8, § 12D(b)(2), line 18):
• Text: “the … tool does not base determinations solely on a group dataset.”
• Analysis: This restricts algorithmic risk-scoring or profiling based solely on aggregated demographic data, arguably to prevent bias.
3. Criteria consistency (Sec. 8, § 12D(b)(3), lines 18–21):
• Text: “the … tool’s criteria and guidelines complies with this chapter, including … sections 12 through 16.”
• Analysis: All AI-based rules must align with the state’s medical necessity and review criteria, ensuring AI doesn’t introduce unauthorized standards.
4. Human-in-the-loop requirement (Sec. 8, § 12D(c), lines 31–36):
• Text: “An AI-based algorithm … shall not be the sole basis of a decision to deny, delay or modify health care services … An adverse determination … shall be made only by a licensed physician or a licensed health care provider …”
• Analysis: Enforces human oversight on all AI determinations; insurers cannot wholly automate denials or approvals.
5. Transparency and auditability (Sec. 8, § 12D(b)(7)–(9), lines 24–31):
• Text:
– “the … tool shall be open to inspection for audit or compliance reviews by the division;”
– “shall disclose … if AI-based algorithms are used … and … criteria, data sets … and the algorithm itself and the outcomes …”
– “the … tool’s performance, use and outcomes are periodically reviewed and revised to maximize accuracy and reliability.”
• Analysis: Requires insurers to make their AI systems transparent to regulators, providers, and enrollees—potentially revealing proprietary models.
6. Non-discrimination and HIPAA compliance (Sec. 8, § 12D(b)(5)–(6), lines 26–29; § 12D(b)(10), lines 35–36):
• Text:
– “the … tool does not discriminate … against enrollees in violation of state or federal law;”
– “is fairly and equitably applied;”
– “patient data is not used beyond … stated purpose, consistent with HIPAA.”
• Analysis: Binds AI tools to civil rights and privacy standards, adding a compliance burden on carriers.
Section D: Enforcement & Penalties
1. Regulatory enforcement (Sec. 8, new § 12E, lines 47–56):
• Text: “The division shall enforce … and shall impose a penalty … If … failing to comply … the commissioner shall notify … impose a corrective action plan. If … non-compliance … the carrier shall be fined up to \$5,000 for each day …”
• Analysis: Non-compliance—including AI transparency or human-in-loop rules—carries substantial daily fines, incentivizing carriers to audit and update AI systems promptly.
2. Alignment with future federal guidance (Sec. 8, § 12D(e), lines 39–42):
• Text: “A carrier … shall comply with applicable federal rules and guidance … The division may issue guidance … within 1 year of the adoption of federal rules …”
• Analysis: Ensures state law can evolve with forthcoming HHS AI regulations, reducing legal uncertainty.
Section E: Overall Implications
• The bill does not directly fund or promote AI R&D but heavily governs AI deployment in health insurance prior authorization.
• By mandating individualized data use, human oversight, and transparency, it curbs purely algorithmic decision-making and may slow adoption of advanced machine-learning tools that rely on population datasets or deep neural networks lacking clear audit trails.
• Startups offering AI-based utilization management platforms will face compliance costs: documenting data provenance, opening models for audit, and layering in physician sign-off.
• Established insurers may need to retool or retire “black-box” AI systems, integrate logging for every decision, and train human reviewers.
• Regulators gain broad powers—audits, corrective action plans, and fines—to enforce AI controls; this may encourage rigorous governance but could deter experimentation.
• End-users (patients and providers) gain transparency rights and assurance of a human reviewer, potentially improving trust but possibly lengthening review times if human capacity is limited.
House - 1210 - An Act relative to AI health communications and informed patient consent
Legislation ID: 88829
Bill URL: View Bill
Sponsors
House - 2236 - Resolve relative to children’s mental health in social media
Legislation ID: 89622
Bill URL: View Bill
Sponsors
House - 495 - An Act reducing emissions from artificial intelligence
Legislation ID: 87735
Bill URL: View Bill
Sponsors
House - 614 - An Act relative to issuing guidance regarding setting policies for the use of AI in schools
Legislation ID: 84523
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a structured analysis of H.614 (2025) “An Act relative to issuing guidance regarding setting policies for the use of AI in schools,” organized per your requested sections. All quotations are drawn verbatim from the enrolled text.
SECTION A: Definitions & Scope
1. “Artificial intelligence (AI) programs”
• Quotation: “guidelines … for school districts and charter schools on the usage of artificial intelligence (AI) programs by students and the utilization of these programs in the classroom by educators to enhance learning.” (Section 1A, inserted text)
• Analysis: The only explicit definition is the parenthetical “(AI)” following “artificial intelligence,” but no further technical delimitation (e.g., machine-learning model, generative AI) is provided. This broad phrasing implicitly covers any software or service described as “AI,” from simple rule-based tutors to large language models.
2. Scope: K–12 public and charter schools
• Quotation: “for school districts and charter schools…” (Section 1A)
• Analysis: The bill’s scope is strictly educational institutions under the Department of Elementary and Secondary Education. It does not apply to higher education, private schools, or non-educational contexts.
SECTION B: Development & Research
There are no provisions in H.614 that mandate or finance AI R&D, data-sharing, or public–private research partnerships. The bill is exclusively concerned with “guidance” on usage and policy, not with research funding, model development, or data infrastructure.
SECTION C: Deployment & Compliance
1. Mandate to issue guidance
• Quotation: “The commissioner shall issue guidelines, based off of available best practices, for school districts and charter schools on the usage of … AI programs …” (Section 1A)
• Analysis: This establishes a soft-regulatory framework. The guidance is not binding law or regulation: it is advisory, intended to inform local district policies.
2. Topics to be covered in guidance
• Quotation: “The guidance shall include the benefits and limitations of AI in education; how to promote the safe use of AI by students and teachers; the equity implications of the use of AI in the classroom; and what factors schools should consider when setting policies concerning the use of AI.” (Section 1A)
• Analysis:
– “Benefits and limitations” encourages balanced, evidence-based discussion.
– “Safe use” could imply cybersecurity, data-privacy, or age-appropriateness, but these terms are undefined and thus leave room for interpretation.
– “Equity implications” signals concern for access disparities (e.g. broadband, device availability) or algorithmic bias, though no standards or metrics are specified.
3. Public engagement requirement
• Quotation: “In developing such guidance, the department shall hold a minimum of three public hearings to gather input from … superintendent groups, school leaders, and teachers …” (Section 1A)
• Analysis: This procedural mandate builds stakeholder buy-in and may surface a range of policy approaches. It also delays guideline publication until after hearings are complete.
4. Publication timeline and updates
• Quotation: “The commissioner shall update said guidelines as necessary and annually publish them on the department’s website no later than September 1.” (Section 1A)
• Analysis: The annual update cycle allows the guidance to keep pace with rapid changes in AI technology and pedagogy, but “as necessary” is broad—no criteria for when updates are warranted.
SECTION D: Enforcement & Penalties
H.614 contains no enforcement provisions, civil or criminal penalties, nor incentives (e.g., grants) linked to compliance. Its mechanism is purely advisory: publication of nonbinding guidelines.
SECTION E: Overall Implications
1. Advancing AI integration in schools
• By mandating an official set of statewide “best practice” guidelines, the bill reduces uncertainty for districts considering AI tools—potentially accelerating adoption where benefits (personalized learning, automated grading) are clear.
2. Ensuring responsible use
• Requiring coverage of “safe use” and “equity implications” helps foreground common concerns (student data privacy, bias) and may steer districts toward guardrails (e.g., parental consent, model audits).
3. Limitations and ambiguities
• No definitions of “AI programs” or “best practices” could lead to uneven interpretations by districts, with some over-restricting or under-supervising.
• Absence of enforcement means districts may ignore the guidance altogether, reducing its practical impact.
4. Effects on stakeholders
• Researchers and vendors: May see clearer demand signals for educational AI tools aligned with state guidance, but also face uncertainty until guidelines are released.
• End-users (teachers/students): Gain a centralized resource for understanding AI’s benefits/risks, but actual classroom experience will vary by district capacity to implement.
• Regulators: The Department of Elementary and Secondary Education must allocate staff time and resources to draft, update, and manage hearings—a modest regulatory expansion but without new enforcement duties.
In sum, H.614 establishes a consultative, annually updated advisory framework for school-level AI policy in Massachusetts. It stops short of binding mandates, leaving districts broad discretion, while signaling that the state sees AI as a significant emerging educational tool requiring centralized guidance.
House - 76 - An Act to protect against election misinformation
Legislation ID: 90041
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of H.76 (“An Act to protect against election misinformation”), organized per your requested structure. Each point is tied to quoted bill language.
Section A: Definitions & Scope
1. “Artificial intelligence”
– Text: “Artificial intelligence, computerized methods and tools, including but not limited to machine learning and natural language processing, that act in a way that resembles human cognitive abilities…” (Defs., lines 1–4)
– Analysis: This catch-all definition explicitly covers modern AI approaches (machine learning, NLP) and zeroes in on systems “that act in a way that resembles human cognitive abilities.” It anchors the bill’s reach to any software or service with humanlike problem-solving.
2. “Generative artificial intelligence”
– Text: “Generative artificial intelligence, artificial intelligence technology that is capable of generating content such as text, audio, image, or video based on patterns learned from large volumes of data.” (Defs., lines 5–8)
– Analysis: By calling out “generative” AI, the bill targets models like GPT, DALL·E, Stable Diffusion, deepfake video generators, etc. This definition is narrower than “AI” generally, focusing on creation of new content.
3. “Synthetic media”
– Text: “Synthetic media, audio or video content substantially produced by generative artificial intelligence.” (Defs., lines 19–21)
– Analysis: This term overlaps with “generative AI” but hones in on final outputs—deepfakes, voice clones, AI-generated videos. It signals the bill’s concern with audiovisual misinformation.
4. “Materially deceptive election-related communication”
– Text (partial): “communication in any media… that contains verifiably false information regarding… (v) the express endorsement of a candidate… by … any person.” (Defs., lines 9–18)
– Analysis: Although not an AI definition, this scope phrase links AI-generated content (“synthetic media”) to prohibited election misinformation.
Section B: Development & Research
– The bill contains no provisions mandating AI research funding, reporting by labs, or data-sharing for R&D. There are no R&D carve-outs or university exemptions.
Section C: Deployment & Compliance
1. 90-day pre-election prohibition
– Text: “Except as provided in subsection (d), … shall not, within 90 days of an election … distribute with actual malice materially deceptive election-related communication…” (§ b, lines 1–5)
– Analysis: All actors—“person, candidate, campaign committee… or other entity”—are barred from distributing AI-generated or other deceptive content in a critical period. Any “synthetic media” deepfake endorsement is covered.
2. Intent and malice standard
– Text: “…with actual malice materially deceptive election-related communication with the intent to mislead voters…” (§ b, lines 3–5)
– Analysis: The “actual malice” test (knowingly false or with reckless disregard) raises the bar for enforcement. Deployers of AI tools must audit outputs to avoid reckless misinformation.
3. Exemptions for news and satire
– Text: “This section shall not apply… if the broadcast clearly acknowledges… that such communication is manipulated…” (§ d(2), lines 1–6) and “This section shall not apply… satire or parody.” (§ d(5), lines 11–12)
– Analysis: Legitimate journalists and satirists can use generative AI to create election-related content, provided they include clear disclosures. AI vendors may need to support metadata tagging or watermarking to facilitate these disclosures.
Section D: Enforcement & Penalties
1. Injunctive relief
– Text: “A person whose voice or likeness appears… or the attorney general may seek injunctive or other equitable relief…” (§ c(1), lines 1–4)
– Analysis: Individuals (e.g., a public figure whose deepfake voice is misused) and the AG can halt distribution of AI-generated false content.
2. Civil damages
– Text: “A person… may bring an action for general or special damages… A court may also award… reasonable attorney’s fees and costs.” (§ c(2), lines 1–6)
– Analysis: Private suits incentivize platforms and campaigns to police AI outputs. The “clear and convincing evidence” burden (§ c(3)) requires substantial proof, reducing frivolous claims but still holding bad-faith actors accountable.
3. 47 U.S.C. § 230 immunity preserved
– Text: “This section shall not alter… immunities of an interactive service provider under 47 U.S.C. section 230.” (§ d(1), lines 1–3)
– Analysis: Social media platforms remain shielded from liability for third-party AI content, though they may face injunctions.
Section E: Overall Implications
1. Restrictive impact on AI deployment
– By focusing on “generative AI” and “synthetic media” in the context of election communications, the bill places a legal risk on any state-level deployment of AI that could produce false election-related content. AI developers and users must build detection, watermarking, or content-verification into their pipelines to avoid “actual malice” distribution.
2. Minimal effect on AI research
– Lack of R&D, data-sharing, or reporting mandates means the state’s research community is largely unaffected. The bill does not impose testing requirements on AI labs.
3. Platform and campaign compliance burden
– Campaigns, third-party aggregators, and platforms will need compliance teams to review AI-generated materials, label synthetic media clearly, and maintain records demonstrating lack of “actual malice.”
4. Potential chilling of innovation
– The 90-day blackout period around elections may dissuade startups from offering generative AI tools to civic-tech ventures unless they invest in robust content controls.
5. Enforcement balance
– Private right of action plus AG enforcement creates a dual enforcement regime; however, the “clear and convincing” standard and § 230 carve-out for platforms moderate over-reach.
Ambiguities
– “Substantially produced” by AI (§ def. of Synthetic media): Could be interpreted to include human-edited AI outputs or only fully synthetic content.
– The exact threshold for “actual malice” in digital contexts remains unsettled—campaigns may need legal guidance on acceptable risk.
House - 77 - An Act fostering artificial intelligence responsibility
Legislation ID: 89684
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI–focused analysis of H.77, “An Act fostering artificial intelligence responsibility.” All page– and section–references point to the bill as introduced in the 2025–26 General Court.
Section A. Definitions & Scope
1. Automated Decision System (ADS)
• “Automated Decision System (ADS)” is defined at § 1(a):
– “any computational process, automated system, or algorithm utilizing machine learning, statistical modeling, data analytics, artificial intelligence, or similar methods that issues an output … used to assist or replace human decision making on decisions that impact natural persons.”
• Relevance: this umbrella term captures nearly all modern AI/ML-driven tools.
2. Automated employment decision tool
• Later cross-referenced as “automated decision tool” (see § 1(a) and Sec 3(a)).
• Targets AI systems used in hiring, promotion, discipline, etc.
3. ADS output
• “any information, data, assumptions, predictions, scoring, recommendations, decisions, or conclusions generated by an ADS” (§ 1(a)).
• This clause makes clear that trained models’ predictions fall under regulation.
4. Vendor and Employer
• Vendor (§ 1(a)) includes any developer or distributor of an automated employment decision tool.
• Employer (§ 1(a)) extends to any person or entity that uses these tools in Massachusetts.
5. Impact assessment, independent auditor, meaningful human oversight (§ 1(a))
• “Impact assessment” (§ 1(a)) and related duties (§§ 2(j), 3(a)) require third-party audits of ADS before deployment.
• “Independent auditor” (§ 1(a)) must have no conflicts with vendor or tool.
• “Meaningful human oversight” (§ 1(a)) mandates that a qualified human review AI outputs.
Section B. Development & Research
H.77 contains no provisions that directly allocate funding, mandate data-sharing for research, or impose reporting requirements on R&D institutions. The bill’s focus is strictly on the deployment and use of AI in employment and by state agencies, not on basic AI research.
Section C. Deployment & Compliance
1. Employer use of electronic monitoring (§ 2)
• § 2(a)(i)–(vi): employers may only deploy “electronic monitoring tools” for narrow, enumerated purposes (e.g., “ensuring the quality of goods and services,” “conducting periodic assessment of worker performance”).
• § 2(b): requires prior written notice and consent from employees and candidates, e.g.:
– “a description of whether and how any employee data collected by the electronic monitoring tool will alone or in conjunction with an automated employment decision tool be used to make an employment decision” (§ 2(b)(v)).
2. Impact assessments for electronic monitoring (§ 2(j))
• § 2(j)(i)–(vii): electronic monitoring must undergo an impact assessment by an independent party within one year before deployment (or six months from enactment if already in use).
• Assessment must “identify which allowable purpose(s)” and describe privacy, bias, or legal risks.
3. Automated decision tools in employment (§ 3(a)–(g))
• § 3(a)(i)–(xiii) details extensive requirements for initial impact assessments of any “automated employment decision tool.” These include:
– “evaluate whether those attributes and techniques are a scientifically valid means of evaluating an employee or candidate’s performance” (§ 3(a)(iv));
– “consider, identify, and describe any disparities in the data used to train or develop the tool … and what actions may be taken … to reduce or remedy any disparate impact” (§ 3(a)(v)).
• § 3(b): annual re-assessments.
• § 3(c): record-keeping and documentation requirements, including historical versions of the tool.
• § 3(e): if a disparate-impact finding occurs, employers must “refrain from using the tool until it takes reasonable and appropriate steps to remedy that disparate impact.”
4. Notice requirements (§ 4)
• § 4(a)(i)–(vi): employers must notify employees and candidates at least ten business days before using an automated employment decision tool, including:
– “the job qualifications and characteristics that such automated employment decision tool will assess” (§ 4(a)(ii));
– “the results of the most recent impact assessment … or information about how to access that information if publicly available” (§ 4(a)(iv)).
• § 4(b): notice must appear in job postings, on websites, and be provided in accessible formats.
5. Restricted uses (§ 5)
• § 5(a)(iii)–(v): prohibits ADS from “mak[ing] predictions about … behavior, beliefs, intentions, personality, emotional state,” or “interfere[ing] with … activity protected under labor and employment law.”
• § 5(b): “shall not rely primarily on output from an automated decision tool when making hiring, promotion … decisions,” and requires meaningful human oversight again.
6. State agency purchases & uses (§ 30 § 66 and § 30B § 24)
• § 30 § 66: “Any agency … shall be prohibited from … utilizing any automated decision system … unless such utilization … is specifically authorized in law.”
• § 30B § 24: state procurement of any ADS must be preceded by an impact assessment every two years, including bias, cybersecurity, safety, misuse‐mitigation, and data privacy testing. Agencies must submit assessments 60 days before implementation.
Section D. Enforcement & Penalties
1. Private civil actions (§ 7)
• “An individual subjected to an adverse employment action based on conduct prohibited by this Act may file a civil action against an employer … in their individual capacity. … restitution and consequential damages, as well as liquidated damages … reasonable attorneys’ fees and costs.” (§ 7).
2. AG and agency citations (§ 7)
• Employers who retaliate against workers who assert rights under the chapter “shall be punished or shall be subject to a civil citation or order as provided in section 27C.”
3. Anti-retaliation (§ 6)
• Protects employees who refuse to follow ADS output in good faith where output poses safety, legal, or professional-licensure conflicts (§ 6).
Section E. Overall Implications
• The bill does not incentivize AI innovation but imposes rigorous ex-ante and ex-post compliance burdens on any employer or state agency that uses AI in hiring, monitoring, or decision-making.
• Startups and vendors of employment-related AI must build in audit-friendly architectures, supply extensive documentation, and prepare for repeated third-party impact assessments. This raises the cost and delays time-to-market.
• Researchers in basic AI remain unaffected; no data-sharing mandates or funding provisions appear.
• End-users (workers and candidates) gain robust transparency rights, advance notice, and the ability to challenge AI-driven decisions.
• Regulators (department of labor standards, attorney general, licensing boards) receive explicit rule-making authority and a clear framework for enforcement.
• State agencies face procurement freezes unless explicit legislative authorization of an ADS exists, ensuring minimal deployment of algorithmic decision-making in public benefits, licensing, or other rights-impacting functions.
House - 81 - An Act relative to artificial intelligence disclosure
Legislation ID: 84335
Bill URL: View Bill
Sponsors
House - 83 - An Act establishing a special legislative commission to study load growth due to AI and data centers
Legislation ID: 89120
Bill URL: View Bill
Sponsors
House - 846 - An Act enhancing disclosure requirements for synthetic media in political advertising
Legislation ID: 88916
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a structured, citation-anchored analysis of Bill H.846 (“An Act enhancing disclosure requirements for synthetic media in political advertising”) with emphasis on its AI-related provisions and likely impacts.
Section A: Definitions & Scope
1. “Artificial intelligence” (AI)
Bill text (Ch. 50 § 1, new):
“Artificial intelligence means the capability of a computer system to perform tasks that normally require human intelligence, such as visual perception, speech recognition, content generation, and decision-making.”
Analysis: This broad definition explicitly covers any computing that mimics human cognitive skills, anchoring later provisions to generative systems. It is not limited to “machine learning” or specific architectures.
2. “Generative artificial intelligence” (GenAI)
Bill text (Ch. 50 § 1, new):
“Generative artificial intelligence means artificial intelligence technology capable of creating content such as text, audio, image, or video based on patterns learned from large volumes of data rather than being explicitly programmed with rules.”
Analysis: Targets “pattern-learned” systems (e.g., large-language models, diffusion models). By calling out “text, audio, image, or video,” the text flags the full range of media GenAI can produce.
3. “Synthetic media”
Bill text (Ch. 50 § 1, new):
“Synthetic media means audio or video content substantially produced by generative artificial intelligence.”
Analysis: Narrows the regulatory scope to audio/video outputs of GenAI, excluding purely textual or purely static-image content.
Section B: Development & Research
Bill H.846 contains no provisions that mandate or guide AI R&D funding, reporting, academic-industry data sharing, or public-sector partnerships. Its focus is entirely on disclosure in political advertising.
Section C: Deployment & Compliance
1. Applicability to Political Advertising
Bill text (Ch. 56 § 70(a)): Any paid audio/video communication “intended to influence voting” and “contains synthetic media” must comply.
Analysis: Campaigns, political action committees (PACs), parties, or individuals using contributions are required to label GenAI-generated segments in ads.
2. Disclosure Requirements
Beginning and end labels
• “Include at the beginning and end of the communication the words, ‘Contains content generated by AI’.” (Ch. 56 § 70(a)(1))
On-screen or in-audio overlay
• Throughout each synthetic segment, display or state in legible writing one of:
– “This video content generated by AI,”
– “This audio content generated by AI,” or
– “This content generated by AI.”
(Ch. 56 § 70(a)(2)(i–iii))
Analysis: These rules create a clear, standardized label for voters. For video, on-screen supertitles or voiceovers are required; for audio-only ads (e.g., radio), a spoken tag or transcript inclusion is implied.
Section D: Enforcement & Penalties
1. Civil Penalty
Bill text (Ch. 56 § 70(b)): “Violation … shall be punished by a fine of not more than $1,000.”
Analysis: A relatively modest per-violation cap. Per spot or per campaign? The text does not specify, leaving ambiguity whether each ad run could trigger a separate fine.
2. No Exemption from Other Liability
Bill text (Ch. 56 § 70(b)): “Compliance … does not exempt a person from civil or criminal liability for violations of other applicable law.”
Analysis: Acknowledges that deceptive practices (e.g., defamation or election-fraud statutes) remain enforceable. Labels do not constitute safe harbor against separate legal claims.
Section E: Overall Implications
1. Advance Transparency, Deter Misinformation
By requiring prominent AI‐disclosure, the bill aims to curtail deceptive deep-fakes in campaigns. Voters gain immediate context.
2. Compliance Burden on Campaigns and Vendors
Small campaigns or new AI-video vendors must build labeling into ad production workflows. This may raise barriers to entry or increase costs, especially for rapid content updates.
3. Limited Scope, Low Penalties
The $1,000 maximum fine per violation is unlikely to discourage large, well-funded campaigns, but may impact smaller groups. The bill does not provide a private right of action, so enforcement rests with state election authorities.
4. No Impact on Non-Political AI Use
All R&D, commercial deployment outside political persuasion, and academic AI work remain unaffected. The narrow focus preserves Massachusetts’ wider innovation ecosystem from regulatory spillover.
5. Ambiguities & Future Challenges
“Substantially produced” by AI: Could minor human edits to a deep-fake skirt the definition?
“Throughout the duration”: May be impractical for fast-cut social-media clips under 15 seconds unless platforms offer automated watermarking.
In sum, Bill H.846 is a targeted measure designed to increase transparency in AI-generated political advertisements. It is unlikely to reshape the broader AI R&D or commercial landscape in Massachusetts but will require campaign operators and suppliers of AI-video/audio services to build compliance into their ad production.
House - 90 - An Act regulating provenance regarding artificial intelligence
Legislation ID: 87827
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a structured analysis of House Bill H.90 (“An Act regulating provenance regarding artificial intelligence”) following your requested format. All claims are anchored to direct quotations from the bill text.
Section A: Definitions & Scope
1. “Artificial Intelligence or ‘AI’”
– Text: “Artificial Intelligence or ‘AI’ – a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions…” (Section 1, new §115, lines 1–4)
– Analysis: This definition explicitly targets AI systems that generate outputs via inference, distinguishing AI from simple data-processing or rule-based software. It sets the bill’s scope to any system with “levels of autonomy and adaptiveness after deployment.”
2. “GenAI Model” and “GenAI Tool”
– Text: “GenAI Model – an AI model designed to generate new data or content based on the patterns or structures from its training data… GenAI Tool – a product or feature that provides the outputs from a GenAI model to end users.” (Section 1, new §115, lines 11–15)
– Analysis: These definitions isolate generative AI from other AI capabilities (e.g. classification). The bill therefore applies only to tools that create new content, not to predictive or analytic AI systems.
3. “GenAI Provider”
– Text: “GenAI Provider – an organization that develops a GenAI Tool that is made publicly available for use by Massachusetts residents.” (Section 1, new §115, lines 15–17)
– Analysis: This clause targets commercial vendors and open-source projects alike, as long as their generative tool is accessible in-state.
4. “Provenance Data,” “Synthetic Content,” “Provenance Application Tool,” and “Provenance Reader”
– Text: “Provenance Data – information such as the origin of a piece of Content and the history of modifications to the Content… includes … (a) whether some or all of the content is Synthetic Content; and (b) when there is synthetic Content, the name of the GenAI Provider…” (Section 1, new §115, lines 18–24)
– Analysis: These definitions establish a metadata standard for tracing content lineage, specifically calling out AI-generated (“Synthetic”) material.
Section B: Development & Research
No provisions directly mandate funding, data-sharing, or reporting requirements for AI research institutions. All obligations fall on GenAI Providers post-deployment.
– Implication: The bill does not incentivize or restrict in-lab AI R&D, focusing instead on downstream content provenance.
Section C: Deployment & Compliance
1. Obligations on GenAI Providers
– Text: “A GenAI Provider shall apply Provenance Data… to wholly-generated Synthetic Content generated by the GenAI Provider’s GenAI Tool.” (Section 1, new §115, lines 26–29)
– Text: “A GenAI Provider shall make available a Provenance Application Tool that enables the user to apply Provenance Data … to Content that has been modified to include Synthetic Content.” (lines 30–33)
– Text: “A GenAI Provider shall make available to the public, a Provenance Reader.” (lines 33–34)
– Analysis: Vendors must embed or supply third-party solutions for attaching and reading provenance metadata. This could constrain smaller startups lacking resources to implement compliant tools, but may encourage a market for third-party “provenance services.”
2. Obligations on Large Online Platforms
– Text: “A Large Online Platform shall (a) retain any available Provenance Data in Content …; and (b) make available to a consumer of Content either (1) the Provenance Data; or (2) a conspicuous indicator that Provenance Data is available, or both.” (lines 35–40)
– Analysis: Platforms like Facebook, YouTube, Twitter or search engines must preserve and surface provenance. This could drive changes to content-management pipelines and user interfaces.
3. Obligations on Capture Devices
– Text: “A Capture Device must (a) include in the Device’s default capture app the ability for a user to enable the inclusion of Provenance Data … (b) Ensure secure hardware-based provenance capture is available to 3rd party applications.” (lines 41–45)
– Analysis: Camera and phone manufacturers need to build in provenance tagging at the point of capture. This extends the bill’s reach from software to hardware, potentially affecting Apple, Samsung, etc.
Section D: Enforcement & Penalties
The text of §115 contains no explicit enforcement mechanisms, penalties, or fines for non-compliance.
– Analysis: Without specified sanctions, enforcement would likely rely on existing consumer-protection or trade-regulation authorities under Chapter 93. The absence of penalties raises ambiguity about compliance incentives and could hinder practical enforcement.
Section E: Overall Implications
1. Advance Transparency in Generative AI
– By defining “Provenance Data” and obligating both producers and distributors to tag and preserve it, the bill seeks to make AI-generated or modified media readily identifiable.
2. Market Creation for Metadata Tools
– Startups and standards bodies may be incentivized to develop compliant “Provenance Application Tools” and “Provenance Readers,” potentially leading to mass adoption of interoperable metadata schemas.
3. Burden on Small Providers and Device Makers
– Smaller GenAI Providers and hardware manufacturers could face development costs to meet these requirements, possibly disadvantaging entrants without economies of scale.
4. Regulatory Ambiguity
– The lack of explicit enforcement procedures or penalties creates uncertainty for all stakeholders. Until regulatory agencies issue guidance or regulations, compliance timelines and acceptable implementation methods remain unclear.
5. Ecosystem Shift
– If enacted, Massachusetts would join a small set of jurisdictions mandating provenance for AI content, potentially influencing broader national or international standards.
House - 94 - An Act to ensure accountability and transparency in artificial intelligence systems
Legislation ID: 84736
Bill URL: View Bill
Sponsors
House - 97 - An Act protecting consumers in interactions with artificial intelligence systems
Legislation ID: 85624
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an analysis of Bill H.97 (“An Act protecting consumers in interactions with artificial intelligence systems”) organized into the sections you requested. All quotations cite section and subsection numbers exactly as they appear in the draft bill.
Section A: Definitions & Scope
1. “Artificial intelligence system”
– Definition: “any machine-based system that, for any explicit or implicit objective, infers from the inputs the system receives how to generate outputs, including content, decisions, predictions, or recommendations…”
• (§ 1, lines 8–12)
– Relevance: explicitly targets software/services that learn from data and produce results—i.e. modern AI/ML products.
2. “High-risk artificial intelligence system”
– Definition: “any artificial intelligence system that, when deployed, makes, or is a substantial factor in making, a consequential decision.”
• (§ 1, lines 27–30)
– Exclusions list common IT tools (spreadsheets, firewalls, etc.) unless they drive “consequential decisions.”
• (§ 1, lines 30–47)
– Relevance: carves out special rules for AI used in areas like lending, hiring, health care (see “consequential decision” below).
3. “Algorithmic discrimination”
– Definition: “any condition in which the use of an artificial intelligence system results in…differential treatment or impact that disfavors an individual or group…on the basis of…protected classification.”
• (§ 1, lines 1–7)
– Relevance: central risk the bill aims to prevent.
4. “Consequential decision”
– Definition: a decision with “material legal or similarly significant effect” on consumer’s education, employment, finance, health-care, housing, insurance, legal services.
• (§ 1, lines 13–21)
– Relevance: ties AI oversight to high-stakes uses.
5. “Developer” vs. “Deployer”
– Developer: “a person…that develops or intentionally and substantially modifies an AI system.”
• (§ 1, lines 22–24)
– Deployer: “a person doing business in this state that deploys a high-risk AI system.”
• (§ 1, line 25)
– Relevance: assigns distinct duties around testing, documentation, and monitoring.
Section B: Development & Research
1. Developer duty to avoid discrimination
– “shall use reasonable care to protect consumers from any known or reasonably foreseeable risks of algorithmic discrimination…”
• (§ 2(a), lines 1–4)
– Rebuttable presumption: compliance with AG rules = “reasonable care.”
• (§ 2(a), lines 4–7)
2. Mandatory documentation & disclosure
– Within 6 months, developers must provide deployers with:
• “general statement describing…foreseeable uses and known harmful or inappropriate uses” (2(b)(1)).
• “documentation disclosing high-level summaries of data used to train…, known or foreseeable limitations…, the purpose…, intended benefits…” (2(b)(2)(i–v)).
• “documentation describing how…evaluated for performance and mitigation of algorithmic discrimination…” (2(b)(3)(i–v)).
• (§ 2(b), lines 8–26)
– Relevance: forces transparency around data, testing, limitations—crucial for research reproducibility and third-party audits.
3. Public transparency inventory
– Developers must post on their website “a statement summarizing: (i) types of high-risk AI systems developed…; and (ii) how the developer manages known or reasonably foreseeable risks…”
• (§ 2(d), lines 1–9)
– Relevance: creates public registry of AI offerings—supports market visibility and accountability.
4. Ongoing reporting to Attorney General
– Must notify AG and deployers of any “known or reasonably foreseeable risks” discovered post-launch, within 90 days of discovery or credible report.
• (§ 2(e), lines 1–10)
– Relevance: mandates continuous monitoring by developers, akin to post-market surveillance in regulated industries.
Section C: Deployment & Compliance
1. Deployer risk management program
– Within 6 months, deployers “shall implement a risk management policy and program…to identify, document, and mitigate known or reasonably foreseeable risks of algorithmic discrimination.”
• (§ 3(b)(1), lines 1–8)
– Must align with NIST AI Risk Management Framework, ISO/IEC 42001, or AG-designated equivalent.
• (§ 3(b)(1)(i), lines 8–15)
– Relevance: imports best-practice standards into state law, binding even small-scale users.
2. Impact assessments
– Initial and annual assessments required, plus within 90 days of any “intentional and substantial modification.”
• (§ 3(c)(1–2), lines 1–16)
– Must document purpose, risks of discrimination, data categories, performance metrics, transparency measures.
• (§ 3(c)(2)(i–vii), lines 7–21)
– Relevance: echoes EU’s AI Act obligations—driving internal and audit-ready records.
3. Consumer notice & adverse-decision disclosures
– Before any consequential decision: notify consumer AI is in use; describe purpose, contact info, “plain-language” system description; opt-out rights.
• (§ 3(d)(1)(i–iii), lines 1–11)
– If decision is adverse: disclose principal reasons, AI’s contribution level, data types/sources; allow data correction; allow appeal with human review.
• (§ 3(d)(2)(i–iii), lines 11–25)
– Relevance: establishes a “right to explanation” and opt-out—could slow automated decisioning but increase consumer trust.
4. Exemptions for small deployers
– Entities <50 employees, not using own data to train AI, may skip risk program and assessments if using systems “for intended uses” and relying on developer’s impact assessment.
• (§ 3(f), lines 1–12)
– Relevance: lightens burdens on small businesses/startups.
Section D: Enforcement & Penalties
1. Attorney General as sole enforcer
– “attorney general has exclusive authority to enforce this chapter.”
• (§ 6(a), line 1)
– Violations are “unfair trade practice[s]” under Chapter 93A—civil penalties, injunctive relief.
• (§ 6(b), lines 1–3)
2. Affirmative defense for self-testing & cure
– “If the developer, deployer…discovers and cures a violation…through feedback, adversarial testing, or internal review…and…is otherwise in compliance with NIST/ISO standards…”
• (§ 6(c)(1–2), lines 1–12)
– Relevance: incentivizes proactive compliance and red-teaming.
3. No private right of action
– “this chapter does not provide…a private right of action for violations.”
• (§ 6(f), lines 3–5)
– Relevance: only AG can sue—limits litigation risk for developers/deployers but may slow enforcement.
Section E: Overall Implications
1. Advancing transparency and accountability
– Developers and deployers must document data sources, biases, testing, and share with both regulators and consumers.
2. Compliance costs & capacity building
– Small entities benefit from limited exemptions, but mid-sized startups may face new overhead (impact assessments, risk programs).
3. Alignment with international norms
– Ties risk management to NIST and ISO frameworks—eases compliance for global AI vendors.
4. Consumer empowerment
– Notice, explanation, and appeal rights promote trust but could slow automated workflows.
5. Regulatory clarity vs. litigation risk
– Exclusive AG enforcement and no private right reduce fear of lawsuits but put burden on a single office—will require resource allocation.
6. Potential ambiguities
– Terms like “reasonable care” and “reasonably foreseeable risks” are not precisely defined—may lead to uncertainty until AG issues rules (§ 7) or court interpretations.
In sum, H.97 establishes a comprehensive state-level regime for high-risk AI, focusing on anti-discrimination, transparency, and risk management while offering small-business carve-outs and aligning with existing federal/international standards.
Senate - 1403 - An Act relative to reducing administrative burden
Legislation ID: 86672
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. “Artificial intelligence” definition
• Citation: Section 8, new Section 12D(a), lines 165–172
– “For purposes of this subsection, ‘artificial intelligence’ means an engineered or machine-based system … that can, for a given set of human-defined explicit or implicit objectives, make predictions, recommendations or decisions influencing real or virtual environments.”
– Analysis: This is the sole explicit definitional scope statement for AI. It is quite broad, covering any “engineered or machine-based system” that performs predictive or decision-making tasks.
2. Applicability of AI provisions
• Citation: Section 8, new Section 12D(d), lines 240–243
– “This section shall apply to utilization review or utilization management functions that prospectively, retrospectively or concurrently review requests for covered health care services.”
– Analysis: The AI rules apply only when carriers or their contractors use AI in utilization review/management.
Section B: Development & Research
– No provisions in this bill promote AI research, development funding, or data-sharing for R&D. All AI text is confined to utilization review processes.
Section C: Deployment & Compliance
1. Transparency & disclosure requirements
• Citation: Section 8, new Section 12D(b)(8), lines 218–222
– “Carriers and utilization review organizations shall disclose … on the carrier’s public website if artificial intelligence-based algorithms are used …; provide … algorithm criteria, data sets used to train the algorithm, the algorithm itself and the outcomes …”
– Impact: Forces vendors/insurers to make AI systems and training data public, which could deter proprietary methods or increase IP risk.
2. Non-discrimination and fairness
• Citation: Section 8, new Section 12D(b)(5)–(6), lines 214–218
– “The artificial intelligence… does not discriminate … in violation of state or federal law;” and “is fairly and equitably applied … in accordance with any … guidance issued by HHS.”
– Impact: Insurers must audit AI for bias, possibly requiring new compliance teams.
3. Auditability & human oversight
• Citation: Section 8, new Section 12D(b)(7), (c), lines 216–223, 243–248
– “The artificial intelligence… shall be open to inspection for audit… by the division;”
– “An artificial intelligence-based algorithm … shall not be the sole basis of a decision … An adverse determination … shall be made only by a licensed physician …”
– Impact: Insurers must maintain human-in-the-loop for final decisions and keep their systems auditable, raising development costs and slowing deployment.
4. Privacy & HIPAA compliance
• Citation: Section 8, new Section 12D(b)(10), lines 226–228
– “Patient data is not used beyond said data’s intended and stated purpose, consistent with the federal HIPAA …”
– Impact: Standard privacy guardrails apply; no new data-sharing for R&D under this act.
5. Alignment with federal standards
• Citation: Section 8, new Section 12D(e), lines 249–253
– “A carrier or utilization review organization subject to this section shall comply with applicable federal rules and guidance … The division may issue guidance … within 1 year of the adoption of federal rules …”
– Impact: Ensures state rules track evolving federal AI guidance, but could delay state updates.
Section D: Enforcement & Penalties
1. Corrective action plans and fines
• Citation: Section 8, new Section 12E, lines 254–264
– “If the commissioner determines … non-compliance … notify the carrier … impose a corrective action plan. If … non-compliance continues … the carrier shall be fined up to $5,000 for each day …”
– Impact: Significant daily penalties create strong incentives for insurers to comply with AI transparency, auditing, and human-in-the-loop rules.
Section E: Overall Implications
– This bill does not foster AI R&D but tightly regulates any AI used in medical claims prior authorization.
– Stricter transparency (public website disclosure of algorithms and training data) and auditability requirements will raise compliance costs for carriers and their AI vendors.
– Mandatory human review of adverse determinations prevents fully automated denials, likely preserving providers’ trust but limiting efficiency gains from AI.
– Daily fines up to $5,000 underscore the regulator’s intent to enforce compliance robustly.
– By anchoring to both state anti-discrimination laws and federal HIPAA and HHS guidance, the bill ensures alignment with existing privacy and fairness norms, reducing legal uncertainty but increasing the compliance burden on smaller AI startups.
Senate - 243 - An Act requiring consumer notification for chatbot systems
Legislation ID: 89666
Bill URL: View Bill
Sponsors
Senate - 264 - An Act establishing protections for consumers interacting with artificial intelligence chatbots
Legislation ID: 88198
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI‐focused analysis of Senate Bill S.264, organized into the five sections you requested. Every citation refers to the bill’s text as filed (Senate Docket No. 2592 / Senate No. 264).
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Section A: Definitions & Scope
––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
1. “Chatbot” (Section 1)
• Text: “Chatbot, an automated program designed to simulate conversation with human users whether through the use of generative artificial intelligence or other similar technology.”
• AI Relevance: This definition explicitly targets AI‐powered conversational agents, including systems that employ “generative artificial intelligence.” By naming generative AI, the Legislature limits the chapter to modern, machine‐learning‐based chat interfaces rather than rule-based scripts alone.
• Scope Implication: Any future variant of conversational AI (“audio, visual, or textual methods”) falls within the law’s ambit.
2. “Commercial entity” (implicit)
• Although not separately defined, Sections 2–4 apply whenever a “commercial entity” deploys a chatbot. Implicitly, “commercial entity” covers businesses offering goods or services, which brings nearly all for-profit AI chatbot deployments under regulation.
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Section B: Development & Research
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No provisions in S.264 directly address AI research or development funding, data sharing, model training, or transparency requirements aimed at the R&D community. The bill’s entire focus is post-development deployment of chatbots by commercial actors.
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Section C: Deployment & Compliance
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1. Mandatory Disclosure (Section 2)
• Text: “Any commercial entity deploying a chatbot shall clearly and conspicuously disclose to the person with whom the chatbot interacts that the person is interacting with a chatbot and not a human.”
• Impact on Startups & Vendors: Chatbot providers must revise user-interface designs to include an explicit notice (e.g., banner, voice prompt).
• Ambiguity: The bill does not specify format, font size, or timing (before first message vs. on demand), leaving room for compliance guidance or litigation over what counts as “clear and conspicuous.”
2. Legal Effect of Chatbot Statements (Section 3)
• Text: “Interactions with, including but not limited to any information or representations provided by, a chatbot deployed by a commercial entity shall have the same legal force and effect as interactions with a person employed by, or acting as an agent of, the commercial entity.”
• Downstream Liability: Companies can no longer disclaim liability by saying “this is an AI” if the chatbot misrepresents contract terms, pricing, or legal rights. This provision extends traditional agency law to AI.
• Startups vs. Incumbents: Smaller firms will face the same breach-of-contract or deceptive-practice risks as established players if their AI makes erroneous promises.
3. Disclaimer Is No Defense (Section 3)
• Text: “Use of a disclaimer shall not constitute a defense under this chapter, chapter 93A, or any other cause of action under the laws of the commonwealth.”
• Compliance Note: Even if a chatbot says “I am not a lawyer” or “for informational purposes only,” those disclaimers cannot shield the deploying entity from claims under Massachusetts’s consumer-protection statutes (Chapter 93A) or other causes of action.
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Section D: Enforcement & Penalties
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1. Treated as Unfair or Deceptive Act (Section 4)
• Text: “a violation of this chapter shall be deemed to be an unfair method of competition and an unfair or deceptive act or practice in the conduct of trade or commerce in violation of section 2 of chapter 93A.”
• Remedies Available:
– Private right of action under Chapter 93A, allowing consumers to seek actual damages, treble damages, and attorney’s fees.
– Attorney General enforcement, potentially leading to cease-and-desist orders or civil penalties.
• Regulatory Burden: Entities must track consumer complaints and maintain records demonstrating “clear and conspicuous” disclosure to mitigate risk.
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Section E: Overall Implications for Massachusetts’s AI Ecosystem
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1. Increased Consumer Trust & Transparency
• By mandating disclosure, S.264 may reduce consumer confusion over AI vs. human interactions, fostering trust in legitimate chatbot uses.
2. Heightened Liability Risk
• Extending full legal agency status to chatbots means any error—mispricing, misrepresentation of services—can trigger Chapter 93A claims. This risk-transfer may push vendors to beef up vetting, auditing, or human-in-the-loop oversight.
3. Disincentive for Over-promising AI Capabilities
• Knowing they can’t hide behind “AI limitations,” companies may dial back overly ambitious claims about what their chatbots can do (e.g., legal or medical advice).
4. Compliance Costs
• Startups and small businesses will need legal reviews, user-interface changes, and staff training to avoid unintended violations. This may favor larger incumbents better capitalized to absorb compliance expenses.
5. Enforcement Uncertainty
• Key terms (“clear and conspicuous,” “chatbot”) lack detailed regulatory guidance. Early enforcement actions or AG opinions will shape the practical contours of compliance.
In sum, while S.264 contains no direct R&D mandates or data-sharing requirements, it significantly reshapes the deployment phase of conversational AI in Massachusetts by demanding transparency and strict liability for chatbot outputs. This likely accelerates adoption of best practices in prompt engineering, oversight, and consumer-facing disclosures—but also raises the bar for compliance, especially for smaller AI vendors.
Senate - 35 - An Act fostering artificial intelligence responsibility
Legislation ID: 86175
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a structured analysis of the AI-related content of Bill S.35 (“An Act fostering artificial intelligence responsibility”). Every claim is anchored to quoted text in the bill. Where the text is ambiguous, I note possible interpretations.
SECTION A: Definitions & Scope
1. “Automated Decision System (ADS)”
1.1 Quotation: “Automated Decision System (ADS), any computational process, automated system, or algorithm utilizing machine learning, statistical modeling, data analytics, artificial intelligence, or similar methods that issues an output … that is used to assist or replace human decision making on decisions that impact natural persons.” (Chapter 149B § 1(a))
1.2 Relevance: This definition explicitly targets any system using AI/ML for decision making—covering recruitment tools, performance scoring, scheduling algorithms, etc. It sets the bill’s perimeter around “ADS” rather than generic software.
2. “Automated employment decision tool”
2.1 Quotation: “Automated decision tool does not include a tool that does not assist or replace employment decision processes and that does not materially impact natural persons … including … a calculator, spreadsheet.” (Chapter 149B § 1(a))
2.2 Ambiguity: “Materially impact” is undefined—could be interpreted narrowly (only safety-critical decisions) or broadly (any effect on job assignment).
3. “ADS output”
3.1 Quotation: “ADS output, any information, data, assumptions, predictions, scoring, recommendations, decisions, or conclusions generated by an ADS.” (Chapter 149B § 1(a))
3.2 Scope: Covers the full pipeline from raw score to final recommendation.
4. “Impact assessment” & “Independent auditor”
4.1 Quotation: “Impact assessment, an impartial evaluation by an independent auditor … ”; “Independent auditor … A person is not an independent auditor … if they … are or were involved in using, developing, offering, licensing, or deploying the automated employment decision tool … ” (Chapter 149B § 1(a))
4.2 Relevance: Introduces a compliance mechanism requiring third-party review of AI systems.
SECTION B: Development & Research
The bill contains no direct funding mandates, data-sharing requirements, or R&D incentives for AI. Its closest provisions address data collection during impact assessments and outline who may serve as “independent auditor.” No state grants or university reporting.
SECTION C: Deployment & Compliance
1. Requirement of Impact Assessments Before Deployment
1.1 Hiring Tools: “It shall be unlawful for an employer to use an automated employment decision tool for an employment decision … unless such tool has been the subject of an impact assessment.” (Chapter 149B § 3(a))
1.2 Frequency: “Impact assessments must … be conducted no more than one year prior to the use … or … within six months of the effective date …” and “subsequent impact assessments each year.” (§ 3(a)–(b))
1.3 Content Requirements:
• “identify and describe the attributes and modeling techniques” (§ 3(a)(iii))
• “evaluate whether … are a scientifically valid means … and whether those attributes may function as a proxy for belonging to a protected class” (§ 3(a)(iv))
• “consider … disparate impact on persons based on race, color, … and what actions may be taken …” (§ 3(a)(v–vi))
• “evaluate whether the use of the tool may limit accessibility for persons with disabilities …” (§ 3(a)(vii))
1.4 Public Registry: “be submitted … to the department for inclusion in a public registry of such impact assessments within sixty days of completion …” (§ 3(a)(xiii))
2. Notice & Transparency to Workers
2.1 Hiring & Evaluation: “Any employer that uses an automated employment decision tool to assess or evaluate an employee or candidate shall notify employees and candidates … no less than ten business days before such use …” (Chapter 149B § 4(a))
2.2 Content: Must describe “job qualifications and characteristics that … tool will assess,” “source of such data,” and “results of the most recent impact assessment …” (§ 4(a)(i–iv)).
3. Restricted Uses
3.1 Prohibited Predictions: “shall not … use an automated decision tool … to make predictions about an employee’s … behavior, beliefs, intentions, personality, emotional state …” (Chapter 149B § 5(a)(iii))
3.2 Bans on Biometric Analytics: “shall not … use an automated decision tool that involves facial recognition, gait, or emotion recognition technologies.” (§ 5(a)(vii))
3.3 Limited Reliance: “shall not rely primarily on output from an automated decision tool when making hiring, promotion, termination … decisions. … A human decision-maker must actually review … and exercise independent judgment …” (§ 5(b))
4. Off-Duty Monitoring
4.1 Employers may not use “ADS … in conjunction with an electronic monitoring tool … to monitor employees who are off-duty and not performing work-related tasks.” (Chapter 149B § 2(d)(iii))
SECTION D: Enforcement & Penalties
1. Civil Remedies
1.1 “An individual subjected to an adverse employment action based on conduct prohibited by this Act may file a civil action against … the employer … If liability is found, the employee … entitled to restitution and consequential damages …, liquidated damages …, pre- and post- judgment interest, reasonable attorneys’ fees and costs … punitive damages.” (Chapter 149B § 7)
2. Anti-Retaliation
2.1 “No employee shall be penalized … for refusing to follow the output of … an AI system … if … the output may … lead to harm … and the employer refused or otherwise failed to adjust the output.” (Chapter 149B § 6(a))
3. Recordkeeping & Audits
3.1 “An employer or its vendor shall retain all documentation pertaining to the design, development, use, and data of an automated employment decision tool that may be necessary to conduct an impact assessment.” (Chapter 149B § 3(c))
3.2 “It shall be unlawful for an independent auditor, vendor, or employer to manipulate, conceal, or misrepresent the results of an impact assessment.” (§ 3(f))
4. State Agency Procurement (Chapter 30B § 24)
4.1 “No … state agency shall authorize any … procurement … of any system utilizing … automated decision systems, except where the use … is specifically authorized in law.”
4.2 Parallel Impact Assessment Requirements for public-sector AI.
SECTION E: Overall Implications for Massachusetts’ AI Ecosystem
1. Restrictive Compliance Burden on Employers & Vendors
• Mandatory third-party audits, annual re-assessments, public registry, recordkeeping, and detailed notice requirements add significant cost and legal exposure for any firm deploying AI in hiring or monitoring.
2. Enhanced Worker Protections
• Explicit bans on biometric emotion/gait/facial recognition, off-duty monitoring, and predictions about beliefs or emotions protect privacy and labor rights.
3. Limited Incentives for R&D
• No grants or pilot-program authorization; researchers and startups must navigate rather than benefit from this law.
4. Public-Sector Caution
• Chapters 30 and 30B force state agencies to either secure legislative authorization or forgo AI deployments, likely chilling government innovation.
5. Enforcement through Civil Actions
• High statutory damages, attorney-fee awards, and anti-retaliation provisions will induce verbose compliance programs but may deter small businesses.
Ambiguities Worth Noting
– “Materially impact”: undefined threshold for what triggers the ADS definition.
– “Least invasive means”: subjective standard open to dispute.
In sum, Bill S.35 tightly regulates commercial and public-sector AI in employment contexts via mandatory impact assessments, transparency, human-in-the-loop requirements, and strong worker remedies—while offering no direct R&D support.
Senate - 37 - An Act promoting economic development with emerging artificial intelligence models and safety
Legislation ID: 88586
Bill URL: View Bill
Sponsors
Senate - 429 - An Act to establish a commission to investigate AI in education
Legislation ID: 89269
Bill URL: View Bill
Sponsors
Senate - 44 - An Act to protect against election misinformation
Legislation ID: 86797
Bill URL: View Bill
Sponsors
Senate - 49 - An Act relative to cybersecurity and artificial intelligence
Legislation ID: 89688
Bill URL: View Bill
Sponsors
Senate - 51 - An Act relative to social media, algorithm accountability, and transparency
Legislation ID: 89523
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of the proposed Massachusetts “Act relative to social media, algorithm accountability, and transparency” (Senate Bill 51). Wherever possible, I quote the exact bill text to anchor each point.
Section A: Definitions & Scope
1. “Algorithm” (Sec. 36 (a))
• Text: “Algorithm, computational process that uses machine learning, natural language processing, artificial intelligence techniques, or other computational processing techniques of similar or greater complexity…”
• AI Relevance: By explicitly tying “algorithm” to ML, NLP, and AI techniques, the bill brings AI-driven recommendation engines squarely within its scope. Any social feed or recommender built on these technologies would be regulated.
2. “Recommendation algorithms” (Sec. 36 (a))
• Text: “…an algorithm will refer to recommendation algorithms, also known as engagement-based algorithms, which passively populate a user’s feed…”
• Scope: This narrows the focus to AI systems that drive content ranking and personalization without user request—classic AI-powered social media feeds.
3. “Covered platform” (Sec. 36 (a))
• Text: “…an internet website, online service… that during the preceding calendar year: (1) controlled or processed the personal information of not less than one hundred thousand consumers… or (2) …twenty-five thousand consumers and derived more than twenty-five per cent of their gross revenue from the sale of personal data.”
• Scope: Any sizable AI-driven service—social or otherwise—that meets these thresholds is subject to the bill, ensuring broad coverage of major tech firms.
4. “Independent third-party auditor” (Sec. 36 (a))
• Text: “Independent third-party auditor, auditing organization that has no affiliation with a covered platform as defined by this section.”
• AI Relevance: Mandates external AI risk assessments rather than self-certification, targeting bias, safety, and algorithmic harms.
5. “Social media platform” (Sec. 36 (a))
• Text (summarized): Must (1) allow users to connect and interact socially and (2) provide profiles, social connections, and user-generated content.
• Scope: Focuses on AI-driven social systems, not search engines or messaging alone.
Section B: Development & Research
This bill contains no direct mandates on AI research funding or data-sharing for R&D. Instead, it focuses on post-development controls:
1. Algorithm Risk Audits (Sec. 36 (d))
• Text: “The office shall… assign independent third-party auditors to conduct algorithm risk audits of covered platforms. Risk audits shall be conducted monthly…”
• Impact on R&D: AI teams will need to instrument models for continual external auditing. This could raise development costs and slow experimentation cycles unless auditing workflows are integrated early.
2. Advisory Council (Sec. 36 (e))
• Text: “…empanel an Advisory Council of experts in the mental health and public policy fields… to review these harms…”
• Research tie-in: May channel platform R&D toward mitigation strategies for identified harms—e.g., research on algorithmic fairness for minors.
Section C: Deployment & Compliance
1. Registration & Fees (Sec. 36 (c))
• Text: “Annually before January 1, covered platforms shall register with the office by providing: (i) a registration fee… (ii) the platform’s name… (iii) physical address… (iv) email; and (v) internet address.”
• Deployment Impact: New administrative overhead. Startups near threshold will need legal/regulatory support.
2. Transparency Reports (Sec. 36 (g))
• Text (excerpt):
– “(i) assessment of whether the covered platform is likely to be accessed by children;”
– “(iii) number of individuals using the covered platform reasonably believed to be children…disaggregated by age ranges…”
– “(v) description of whether and how the covered platform uses system design features to increase, sustain, or extend use…”
• AI Impact: Forces platforms to instrument and report model-driven engagement features—e.g., auto-play, recommendation loops—and quantify time-on-platform by age. This transparency may discourage “addictive” AI patterns.
3. Harm Benchmarks & Mitigation (Sec. 36 (h))
• Text (excerpt): “By January 1, 2027, covered platforms shall submit preliminary reports… measure the incidence of each of the specific harms… The office… consult with… to set benchmarks… to reduce the harms… produce biannual reports containing… steps taken to mitigate harm… measurements indicating the reduction in harm…”
• Deployment Impact: Creates ongoing compliance cycles requiring data collection on AI outcomes (e.g., rates of anxiety or cyberbullying triggered by recommendations) and implementation of model changes—e.g., style-filters or throttling. Vendors must build monitoring pipelines and governance processes.
4. Redaction & Trade Secrets (Sec. 36 (i))
• Text: “However, to the extent any information… is trade secret, proprietary or privileged, covered platforms may request… redacted… The office will conduct a confidential, in-camera review…”
• Impact on AI: Vendors can protect model architecture or proprietary data-handling processes, but still face disclosure of metrics and high-level features design.
Section D: Enforcement & Penalties
1. Violations (Sec. 36 (j))
• Text: “A covered platform shall be considered in violation… if it (i) fails to register… (ii) materially omits or misrepresents required information… or (iii) fails to timely submit a report.”
2. Civil Penalties (Sec. 36 (j)(1))
• Text: “Liable for a civil penalty not to exceed $500,000 per violation… the court shall consider whether the covered platform made a reasonable, good faith attempt to comply…”
• Impact: Creates strong incentives to establish AI-compliance teams. Repeat noncompliance could become a sizable financial risk for large platforms.
Section E: Overall Implications for Massachusetts’ AI Ecosystem
– Accelerated Maturity of AI Governance: Platforms will need to invest heavily in monitoring, auditing, and compliance infrastructure for any AI-driven recommendation system.
– Barrier to Entry for Startups: Compliance costs (auditor fees, in-house legal/regulatory, reporting tools) may disadvantage smaller firms, potentially consolidating incumbents’ market power.
– Research & Innovation Shifts: R&D is likely to pivot toward “safer by design” AI models (e.g., age-aware or harm-mitigating recommenders) to meet benchmarks.
– Public Accountability: Transparency reporting and public disclosure (with limited redactions) could empower journalists, advocates, and regulators to scrutinize AI impacts on youth.
– Cross-Sector Spillover: Though targeted at social media, the broad definition of “algorithm” could eventually be invoked against other AI-powered consumer services in Massachusetts.
Ambiguities & Interpretations
– “Likely to be accessed” (Sec. 36 (a)) employs several thresholds (10 % audience children, COPPA direction, similarity to child-directed platforms). Platforms whose audience skews just under 10 % may dispute coverage.
– “Harms” categories (Sec. 36 (d)) include mental-health disorders, addiction-like behaviors, exploitation, and “predatory” marketing. Quantifying causation vs. correlation may pose audit challenges.
– Benchmarks Setting (Sec. 36 (h)): The process and metrics for establishing “reduction in harm” are left to AG’s office and auditors—introducing regulatory uncertainty around how aggressive mitigation must be.
Senate - 994 - An Act prohibiting algorithmic rent setting
Legislation ID: 89407
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI‐focused analysis of S.994 (“An Act prohibiting algorithmic rent setting”) organized into the requested sections. Every claim is anchored to a quoted clause from the bill text.
Section A: Definitions & Scope
1. “Algorithmic device” (Sec. 15G(a), lines 3–9)
– Quotation: “‘Algorithmic device’, any computational process, including a computational process derived from machine learning or other artificial intelligence techniques, that processes or calculates nonpublic competitor data for the purpose of advising a lessor or landlord concerning the amount of rent …”
– Analysis: This definition explicitly targets AI/ML–based price‐setting tools, whether proprietary or open-source, as long as they “process or calculate” rental data.
2. “Coordinating function” (Sec. 15G(a), lines 9–17)
– Quotation: “‘Coordinating function’, any action that includes: (i) collecting … prices … from two or more … lessors; (ii) analyzing or processing … through use of a system, software or process that uses computation, including by using the information to train an algorithm; and (iii) recommending rental prices … to a … landlord.”
– Analysis: The bill’s reach extends beyond single‐party automation to any platform or service that aggregates competitor data, trains an AI model, and issues rent‐setting recommendations.
3. “Coordinator” (Sec. 15G(a), lines 17–19)
– Quotation: “‘Coordinator’, any person who operates an algorithmic device, software or data analytics service that performs a coordinating function … including a … landlord performing a coordinating function for their own benefit.”
– Analysis: “Coordinator” can be a third-party SaaS vendor or a landlord using in-house AI. The broad “person” definition sweeps in natural persons, corporations, partnerships, etc.
4. “Nonpublic competitor data” (Sec. 15G(a), lines 19–24)
– Quotation: “‘Nonpublic competitor data’, information that is not widely available or easily accessible to the public … about actual rent prices, occupancy rates, lease start and end dates … derived from … another person that competes in the same market.”
– Analysis: This covers proprietary datasets sold by data brokers, scraped in violation of terms of service, or shared under NDA—anything not in the public domain.
5. “Residential Dwelling Unit” (Sec. 15G(a), lines 26–28)
– Quotation: “‘Residential Dwelling Unit’, any house, apartment, accessory unit or other unit intended to be used as a primary residence in the state.”
– Analysis: The ban applies only to primary-residence rentals, not to short-term vacation rentals or commercial leases.
Section B: Development & Research
There are no provisions in this bill that mandate or direct AI research, data-sharing for research, or public-sector AI development. The bill’s sole focus is on prohibiting certain uses of AI in private rent-setting.
Section C: Deployment & Compliance
1. Ban on Algorithmic Rent-Setting (Sec. 15G(b), lines 29–32)
– Quotation: “In setting the amount of rent … or otherwise determining what amount to charge a tenant … no lessor or landlord shall employ, use or rely upon an algorithmic device or coordinator.”
– Analysis: This is a flat prohibition on deploying AI/ML tools for rent determination. It effectively outlaws any dynamic pricing engine that leverages machine learning or algorithmic assistance for residential rents.
2. Exceptions (Sec. 15G(a), lines 5–9)
– Quotation: “Provided, however that ‘algorithmic device’ shall not include: (i) any report published periodically, but not more frequently than monthly, by a trade association … in an aggregated and anonymous matter; or (ii) a product used for … affordable housing program guidelines …”
– Analysis: Trade-association surveys and government-mandated affordability calculators remain permitted. A startup that offers a monthly market summary without tenant-level or competitor-level granularity would not be covered.
Section D: Enforcement & Penalties
1. Private Right of Action via Chapter 93A (Sec. 15G(c), lines 33–34)
– Quotation: “A violation of this section shall constitute a violation of section 2 of Chapter 93A.”
– Analysis: Chapter 93A is Massachusetts’s consumer-protection statute, allowing plaintiffs (tenants or AG) to sue for “unfair or deceptive acts” with treble damages plus attorneys’ fees. This provides strong financial incentives for tenants to bring suits and for landlords to avoid AI tools.
2. Effective Date (Section 2, lines 1–2)
– Quotation: “This act shall take effect 90 days after its passage.”
– Analysis: A relatively short compliance window. AI vendors and landlords must disable or remove algorithmic pricing features within three months of enactment.
Section E: Overall Implications
1. Restricts AI-Driven Pricing Innovation in Residential Rental Market
– By defining and banning “algorithmic devices” and “coordinators,” the state clamps down on any dynamic pricing engine, demand-forecasting model, or competitor-data aggregator that leverages AI. Startups and established vendors offering “smart rent” solutions will have to exit the residential segment or pivot to permitted products (e.g., monthly reports only).
2. Legal and Compliance Burden on Landlords and Vendors
– Landlords must audit all software used for rent-setting to ensure that no AI/ML components ingest nonpublic competitor data. Vendors will need to certify compliance or face Chapter 93A litigation risk.
3. Ambiguity and Compliance Challenges
– “Machine learning or other artificial intelligence techniques” is left undefined and may capture any statistical model. Landlords may over-comply by dropping basic regression or optimization tools, even if they rely solely on public data. The line between “public” and “nonpublic” competitor data may be disputed (e.g., scraped data vs. paid data feeds).
4. No Positive Incentives for Ethical Innovation
– The bill contains no carve-outs or safe harbors for privacy-preserving AI or differential-privacy techniques, nor does it create a certification or “white-listed” process. It is purely prohibitory, which may discourage broader AI experimentation in housing.
5. Enforcement via Consumer-Protection Law
– Using Chapter 93A leverages an existing enforcement mechanism rather than creating a specialized AI regulatory body. It will rely on private plaintiffs and the Attorney General’s office for enforcement.
In sum, S.994 targets AI-powered rent-setting tools with a broad ban, high-stakes enforcement via Chapter 93A, and limited carve-outs. It places no obligations on research or development but erects a hard line against dynamic, data-driven pricing in the residential rental market.
New York
Assembly - 1205 - Establishes the position of chief artificial intelligence officer
Legislation ID: 54982
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. Definition of “Artificial intelligence” (Sec. 1, new subd. 7, lines 3–24)
– “Artificial intelligence” or “AI” means “(a) a machine-based system…that infers, from the input the system receives, how to generate outputs such as predictions, content, recommendations, or decisions…” (lines 3–8).
– This definition explicitly targets systems that “(i) Sense, interpret…data, text, speech…; (ii) Abstract concepts, detect patterns…; (iii) Apply reasoning, decision logic…to generate options, recommendations…; (iv) Operate autonomously…” (lines 9–24).
– Exclusion: basic tools “such as calculators, spell check tools…that do not materially affect the rights, liberties, safety or welfare of any human.” (lines 25–27).
2. Definition of “Automated decision-making system” (Sec. 1, new subd. 8, lines 4–16)
– “Any software that uses algorithms, computational models, or artificial intelligence…to automate, support, or replace human decision-making” (lines 4–7).
– Includes systems that “process data…and apply predefined rules or machine learning algorithms…generate conclusions, recommendations…or predictions.” (lines 8–11).
– Same exclusion of basic office tools (lines 12–16).
Scope: These definitions set the boundary for everything the bill regulates. Any system meeting the broad AI or automated decision-making definition—especially machine-learning, autonomous, or predictive systems—falls under the new office’s oversight.
Section B: Development & Research
There is no explicit research funding or data-sharing mandate, but the chief AI officer’s duties include:
1. Developing statewide AI policies and governance (Sec. 2, § 102-a(2)(a), lines 32–38):
– “Developing and updating state policy and guidelines on the use, procurement, development, and deployment of artificial intelligence and automated decision-making systems” (lines 32–35).
– Potential impact: Standardizes best practices for R&D procurement across agencies; may raise the bar for compliance.
2. Handbook for use and evaluation (Sec. 2, § 102-a(2)(a)(ii), lines 36–43):
– “Developing and updating a handbook regarding the use, study, development, evaluation, and procurement of systems that use artificial intelligence…consistent with state and federal laws, and national and international standards” (lines 38–43).
– Potential impact: Creates a centralized reference for researchers and vendors, which may accelerate adoption but impose compliance burdens.
3. Risk management plan (Sec. 2, § 102-a(2)(a)(iii), lines 43–49):
– “Developing a risk management plan…for assessing and classifying risk levels…pertaining to the…rights, liberties, safety and welfare of any human…” (lines 43–49).
– Potential impact: Researchers must build in safety and fairness assessments; may slow rapid experimentation but enhance trust.
Section C: Deployment & Compliance
1. Human oversight standards (Sec. 2, § 102-a(2)(a)(iv), lines 49–54):
– “Setting governance standards for human oversight of artificial intelligence and automated systems…including…employee training programs for safe and responsible use” (lines 49–54).
– Impact: Agencies and vendors must implement oversight and training, potentially increasing deployment costs.
2. Public transparency requirements (Sec. 2, § 102-a(2)(a)(v), lines 54–56):
– “Ensuring public access requirements…for each state agency use of automated decision-making systems and artificial intelligence.” (lines 54–56).
– Impact: End-users gain visibility into which systems are in use; vendors may need to disclose proprietary details or redacted summaries.
3. Coordination and procurement tracking (Sec. 2, § 102-a(2)(b–c), lines 1–8):
– “Coordinate…activities…performing any functions using AI tools; coordinate and track…procurement and planning” (lines 1–8).
– Impact: Standardizes purchasing; smaller startups may benefit from aggregated RFPs, but will face uniform requirements.
4. Guidance on discrimination and privacy (Sec. 2, § 102-a(2)(e), lines 11–19):
– “Provide guidance…protecting against discrimination based on race…religion…mitigating risks of misinformation…and impact on the human workforce.” (lines 11–19).
– Impact: Forces developers to incorporate fairness audits; may deter biased models but adds compliance steps.
5. Recommendation to disconnect harmful systems (Sec. 2, § 102-a(2)(f), lines 20–24):
– “Recommend the replacement, disconnection or deactivation of any application…inconsistent with law or harmful to…rights, liberties, safety, and welfare.” (lines 20–24).
– Impact: Creates an enforcement lever; vendors risk shutdown if systems produce unlawful or unsafe outcomes.
6. Testing, evaluation, validation (Sec. 2, § 102-a(2)(g), lines 27–34):
– “Study implications of usage…develop common metrics to assess trustworthiness…minimize performance problems…address intentional misuse.” (lines 27–34).
– Impact: Imposes a requirement that agencies adopt or reference standardized evaluation metrics; may spur a local market for testing services.
Section D: Enforcement & Penalties
Explicit penalties are not prescribed, but enforcement powers are:
1. Audits of AI use (Sec. 2, § 102-a(2)(i), lines 50–59):
– “Investigate and conduct periodic audits…ensure…tools comply with constitution, laws…benefit outweighs risk…system is secure and resistant to…manipulation or malicious exploitation.” (lines 50–59).
– Impact: Regular audits create an ongoing compliance obligation; failure to pass could trigger the disconnection recommendation in (f).
2. Access to information (Sec. 2, § 102-a(3), lines 16–22):
– “Chief AI officer may request and receive…staff and other assistance, information, and resources…to properly carry out its functions.” (lines 16–22).
– Impact: Ensures the office can investigate noncompliant deployments; agencies must comply or face escalation.
No explicit fines or criminal penalties, but the “recommendation to disconnect” and “periodic audits” are soft enforcement tools.
Section E: Overall Implications
– Centralization of AI oversight under a gubernatorial appointee (Sec. 2, § 102-a(1), lines 19–27) positions New York to impose uniform AI governance across hundreds of agencies.
– Definitions (Sec. 1, subds. 7–8) are broad, capturing research prototypes, pilot projects, and production systems, meaning almost any AI activity by the state triggers oversight.
– The handbook, risk management plan, and audit regime will likely raise compliance costs and slow deployment but aim to mitigate harms and build public trust.
– Startups and vendors seeking state contracts will need to align with the chief AI officer’s policies, possibly favoring those with robust fairness, security, and transparency processes.
– Researchers may benefit from clearer standards and an advisory committee (Sec. 3, § 104-a, lines 25–34), but lack of direct funding suggests the bill prioritizes regulation over investment.
– Overall, the bill reshapes New York’s AI ecosystem by embedding governance, transparency, and risk management into every stage of state AI development and deployment—balancing innovation incentives with consumer protection.
Assembly - 1332 - Relates to creating a state office of algorithmic innovation
Legislation ID: 55109
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a structured analysis of A. 1332, “An act to amend the executive law, in relation to creating the office of algorithmic innovation.”
SECTION A: Definitions & Scope
1. Definition of “algorithm” (lines 17–20):
“For the purpose of this article, ‘algorithm’ shall mean any set of computer programming instructions used to complete an objective, including for the purpose of creating technology that performs its own decision making otherwise known as artificial intelligence.”
• This is an explicit, very broad definition that captures both traditional rule-based software and modern AI/ML systems.
• Ambiguity: “used to complete an objective” could be read to include everything from simple scripts to advanced neural networks.
2. Scope of the Office (lines 7–14):
“The office shall have as its primary purpose the creation of policies and standards to ensure algorithms are safe, effective, fair, and ethical, and that the state is conducive to promoting algorithmic innovation. The office shall have the power to set standards for algorithms used in any technology, be able to audit all algorithms, and set statewide policy on the use of algorithms and promotion of innovation.”
• Explicitly covers “any technology” embedding algorithms/AI.
• Scope: policy creation, standard-setting, auditing, and statewide guidance.
SECTION B: Development & Research
There are no direct funding mandates, reporting requirements, or data-sharing rules in the text. However:
– Mandate to “promote algorithmic innovation” (line 10–11): suggests the office may establish grant programs or partnerships, though none are spelled out.
– Hiring authority (line 14–16): “The director shall appoint staff and perform such other functions to ensure the efficient operation of the office within the amounts made available therefor by appropriation.”
• Implicit R&D effect: staff could include technical experts to advise researchers and entrepreneurs.
SECTION C: Deployment & Compliance
1. Standard-Setting Power (lines 12–13):
“The office shall have the power to set standards for algorithms used in any technology…”
• Potential to require certification, performance testing, or risk assessments before deployment.
2. Auditing Authority (lines 12–13):
“…be able to audit all algorithms…”
• Implies the office can inspect code, data, or decision outputs.
• Ambiguity: no process is defined (e.g., voluntary self-audit vs. mandatory government inspection; penalty for non-compliance).
3. Statewide Policy (lines 13–14):
“…and set statewide policy on the use of algorithms and promotion of innovation.”
• Could lead to binding rules for procurement (e.g., state agencies must follow guidelines) and possibly private sector obligations.
SECTION D: Enforcement & Penalties
– The act grants broad auditing and standard-setting powers but contains no explicit enforcement mechanisms, fines, or penalties for non-compliance.
– No mention of civil or criminal liability, cease-and-desist orders, or judicial review procedures.
– Incentives: by emphasizing “promotion of innovation” (line 11) and lacking punitive language, the office may rely on guidance and voluntary compliance rather than sanctions.
SECTION E: Overall Implications for New York’s AI Ecosystem
1. Centralized Governance: Establishes a single statewide authority on algorithms/AI, which could streamline policy coherence across agencies.
2. Innovation vs. Oversight Tension: The dual mandate to foster “innovation” while ensuring algorithms are “safe, effective, fair, and ethical” may lead to conflicts in resource allocation and prioritization.
3. Regulatory Ambiguity: Without detailed processes for audits or enforcement, regulated entities (startups, vendors) may face uncertainty about when and how standards will apply.
4. Impact on Stakeholders:
– Researchers & Startups: Potential access to state-led guidance and funding, but unclear compliance burdens could deter small innovators.
– Established Vendors: May need to adapt products to new standards and prepare for audits by a state office.
– End-Users & Civil Society: Could benefit from increased algorithmic transparency and fairness safeguards, assuming the office publishes clear guidelines.
– Regulators: Will gain technical capacity and authority but must still flesh out detailed rules, procedures, and resource needs.
In summary, A. 1332 explicitly embraces artificial intelligence under its broad “algorithm” definition, creates a powerful new oversight office, and sets high-level mandates to both promote and police AI. The lack of procedural detail leaves major implementation questions—especially around enforcement, stakeholder obligations, and resource commitments—open for future rulemaking.
Assembly - 1338 - Relates to the admissibility of evidence created or processed by artificial intelligence
Legislation ID: 55115
Bill URL: View Bill
Sponsors
Assembly - 1342 - Requires the collection of oaths of responsible use from users of certain generative or surveillance advanced artificial intelligence systems
Legislation ID: 55119
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an analysis of Assembly bill A. 1342 (2025–2026 session), which would add section 394-cccc to New York’s General Business Law. All quotations are from the bill text as introduced (LBD04104-01-5).
SECTION A: DEFINITIONS & SCOPE
1. “Advanced artificial intelligence system” (lines 3–11)
• Text: “‘Advanced artificial intelligence system’ shall mean any digital application or software…that autonomously performs functions traditionally requiring human intelligence.”
• Relevance: This is the bill’s umbrella term for AI. By focusing on autonomy, learning, adaptation, decision-making and other “cognitive processes” (lines 7–11), the bill targets software with machine-learning or inference capabilities—i.e. modern AI.
2. “Generative or surveillance advanced artificial intelligence systems” (lines 12–22)
• Text: These are systems which can:
(i) “generate synthetic images, videos, audio or other synthetic media that exhibit accuracy that is indistinguishable from the source…” (lines 14–16);
(ii) “modify or alter existing images, video, or audio…” (lines 17–19);
(iii) “generate synthetic written language…” (lines 20–22); or
(iv) “be used as a tool to surveil a person or persons without their consent.” (lines 22–23)
• Relevance: This definition explicitly covers “deepfake” generators, text-generators like large language models, and surveillance tools that identify or track people. It thus zeroes in on AI functions with high potential for misuse in misinformation, defamation, privacy invasion, etc.
3. “Operator” (lines 24–31)
• Text: “Operator shall mean the person or persons…who distribute and have control over the development of a generative or surveillance advanced artificial intelligence system.” (lines 25–28)
• Relevance: By defining “operator” to include both developers and distributors—and for open-source projects, the hosting platforms—this term brings most AI providers (commercial vendors, open-source maintainers, cloud-hosters) under the bill’s obligations.
4. “Open code” (lines 31–35)
• Text: “Software whose code is made available to the public…with the ability to use, modify, or distribute the code…”
• Relevance: This ensures that open-source AI systems are not exempt merely because they are public-domain or free-licensed. The bill still treats them as “generative or surveillance” systems if they meet the criteria.
SECTION B: (No R&D-Specific Provisions)
The bill contains no mandates on research funding, data-sharing or reporting for AI development. It focuses entirely on user-facing obligations once an “operator” has deployed an AI system to New York residents.
SECTION C: DEPLOYMENT & COMPLIANCE
1. Mandatory Account Creation & Oaths (lines 11–19 of § 394-cccc(2))
• Text: “Every operator…shall require a user to create an account prior to utilizing such service. Prior to each user creating an account, such operator shall present the user with a conspicuous…document that the user must affirm under penalty of perjury…”
• Relevance: Any startup or established AI-vendor providing generative/surveillance AI in New York must build an account-registration flow that includes this sworn statement. It applies to web, mobile, desktop or on-premises interfaces accessible to New York residents.
2. Content of the Oath (lines 18–36 of § 394-cccc(2))
• Text (abridged): “I…affirm under penalty of perjury that I have not used, am not using…this advanced artificial intelligence system in a manner that violates…[i] will not…create or disseminate content that can foreseeably cause injury…aid…illegal activity…disseminate…defamatory, offensive, harassing…content…[or] create…content…of public interest…that I know to be false…for the purpose of misleading the public or causing panic.”
• Relevance: This imposes a broad self-certification by users that they will not misuse the system. It effectively attempts to push downstream legal compliance from the operator onto every end-user and to deter misuse by threat of perjury.
3. Restriction on Modifying the Oath (lines 48–49 of § 394-cccc(5))
• Text: “No operator shall be entitled to augment, add or delete any provisions of such oath except as permitted by the attorney general.”
• Relevance: Operators cannot tailor or simplify the oath. They must use the exact text or seek AG approval—adding administrative overhead for product teams.
SECTION D: ENFORCEMENT & PENALTIES
1. Perjury Liability for Users (lines 37–40 of § 394-cccc(3))
• Text: “Such statement shall be sworn or subscribed…bear a form notice that false statements made therein are punishable as a class A misdemeanor pursuant to section 210.45 of the penal law.”
• Impact: New York users who lie on the oath risk criminal charges—up to 1 year in jail. This is a novel attempt to deter misuse via criminalizing user statements.
2. Operator Reporting Requirement (lines 41–46 of § 394-cccc(4))
• Text: “Every operator…shall submit a copy of each oath taken…to the attorney general within thirty days…Any operator who knowingly fails to submit such oaths…shall be fined three times such amount of profit derived from the user…or three thousand dollars, whichever is greater, per oath.”
• Impact:
– Data Burden: Operators must store and transmit every user’s identity and signed oath to the AG—raising privacy, security and compliance costs.
– Financial Penalties: Fines of “3× profits or $3,000” per missing oath can scale to severe commercial liability for any failure in automation, logging or AG-integration.
3. Non-liability for Users (lines 50–52 of § 394-cccc(6))
• Text: “This section shall not be construed as imposing any liability on a user for an operator’s failure…”
• Impact: Users cannot be sued merely because the operator forgot to collect or submit a required oath. Liability falls entirely on operators.
4. AG Rule-making (lines 53–54 of § 394-cccc(7))
• Text: “The attorney general shall promulgate all rules and regulations necessary…”
• Impact: The AG will define forms, procedures, potential exemptions, and enforcement mechanisms by regulation—introducing further uncertainty until finalized.
SECTION E: OVERALL IMPLICATIONS
1. Barriers to Entry & Innovation
• Small AI providers and open-source projects will face disproportionate compliance costs (identity verification, per-user storage, secure transmission to the AG). This may discourage new or niche generative-AI offerings in New York.
2. Privacy & Data Security
• Operators will collect and transmit users’ names, addresses, and entire sworn statements to the AG. This raises significant data-protection concerns, potential leak risks, and supervisory costs.
3. Chilling Effects on Use & Research
• The perjury threat may deter legitimate uses (e.g., academic or artistic) if users fear criminal liability for supposed “false” statements about intent. It may also hamper benign research into misinformation or deceptive deepfakes.
4. Enforcement Uncertainty
• Enforcement thresholds are vague (e.g., what constitutes “knowingly” failing to submit an oath?). Operators must await AG regulations for clarity, creating a window of legal risk.
5. Shift of Liability Downstream
• By making end-users swear they will not misuse the system, the bill shifts much legal exposure onto individuals rather than incentivizing operators to build stronger technical safeguards (watermarking, rate-limits, content filters).
In sum, A. 1342 expressly targets “generative or surveillance advanced artificial intelligence systems” (§ 394-cccc(1)(b)), imposes a novel per-user-per-jury framework (§ 394-cccc(2)–(4)), and saddles operators with heavy reporting and penalty obligations (§ 394-cccc(4)). It is likely to restrict deployment of AI tools in New York more by procedural/accounting burdens than by technical or substantive safety standards.
Assembly - 1456 - Relates to the use of artificial intelligence for utilization review
Legislation ID: 55233
Bill URL: View Bill
Sponsors
Assembly - 1509 - Requires publishers of books created with the use of generative artificial intelligence to contain a disclosure of such use
Legislation ID: 55286
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an analysis organized into the five requested sections. Each point is tied directly to quoted language from the bill. Where the text does not address a category (for example, research funding), I note that no provision exists.
Section A: Definitions & Scope
1. “Any book . . . created through the use of generative artificial intelligence” (lines 4–7)
– “Any book that was wholly or partially created through the use of generative artificial intelligence . . . shall conspicuously disclose upon the cover of the book, that such book was created with the use of generative artificial intelligence.” (§ 338.1, lines 4–7)
– Relevance: This provision directly targets works produced in whole or in part by an AI system, requiring a disclosure label.
2. “Books subject to the provisions of this section . . . printed and digital books” (lines 8–11)
– “Books subject to the provisions of this section shall include, but not be limited to, all printed and digital books . . . consisting of text, pictures, audio, puzzles, games or any combination thereof.” (§ 338.2, lines 8–11)
– Scope: Covers any medium or format in which a “book” might appear, whether traditional text or multimedia.
3. Definition of “generative artificial intelligence” (lines 12–23)
– The bill provides a multi-part definition:
a. “Any artificial system that performs tasks under varying and unpredictable circumstances without significant human oversight, or that can learn from experience and improve performance when exposed to data sets.” (§ 338.3(a), lines 17–20)
b. “An artificial system developed in computer software, physical hardware, or other context that solves tasks requiring human-like perception, cognition, planning, learning, communication, or physical action.” (§ 338.3(b), lines 20–23)
c. “An artificial system designed to think or act like a human, including cognitive architectures and neural networks.” (§ 338.3(c), lines 1–3)
d. “A set of techniques, including machine learning, that is designed to approximate a cognitive task.” (§ 338.3(d), lines 3–5)
e. “An artificial system designed to act rationally, including an intelligent software agent or embodied robot that achieves goals using perception, planning, reasoning, learning, communicating, decision making, and acting.” (§ 338.3(e), lines 5–8)
– Implicit targeting: These broad criteria encompass virtually any LLM, image-generator, or multimodal AI tool used in producing creative content.
Section B: Development & Research
– No provisions in the bill address funding, grants, data-sharing, or other forms of support or reporting for AI research.
– There is no mandate for research institutions or developers to track AI-generated content beyond the disclosure requirement at publication time.
Section C: Deployment & Compliance
1. Disclosure requirement on covers (§ 338.1, lines 4–7)
– “Conspicuously disclose upon the cover of the book, that such book was created with the use of generative artificial intelligence.”
– Impact on publishers: They must implement a workflow step to identify AI-assisted works and design a visible notice on front or back covers.
– Ambiguity: “Conspicuously” is undefined—publishers may dispute what size, font, or placement satisfies the requirement.
2. Format-neutral “books” (§ 338.2, lines 8–11)
– Requires compliance for both printed and digital editions, which could affect e-book platforms, apps, and websites that host interactive content.
Section D: Enforcement & Penalties
– The bill text contains no explicit enforcement mechanism (e.g., fines, injunctive relief) or designated enforcement agency.
– Because it amends the General Business Law, enforcement might fall under the New York Attorney General’s general authority to police unfair or deceptive practices, but this is not stated.
– Ambiguity: Without a penalty provision, it is unclear whether failure to disclose is a civil violation, misdemeanor, or subject to other sanctions.
Section E: Overall Implications
1. Transparency & Consumer Protection
– By forcing a visible AI-use label, the bill aims to ensure readers know when content is machine-assisted. This could deter misleading claims of fully human authorship.
2. Administrative Burden on Publishers
– Publishers (from large houses to indie presses) must establish processes to track AI usage in writing, editing, illustration, or formatting.
3. Potential Chilling Effect on Creative AI Use
– Some creators and publishers may avoid generative AI tools altogether rather than add a disclosure label that might be viewed unfavorably by certain audiences.
4. Gaps & Uncertainties
– The lack of defined penalties leaves enforcement uncertain.
– “Conspicuous disclosure” is subjective and could generate litigation over font size, cover placement, or wording.
– No carve-out for human-edited or hybrid workflows—works with minimal AI assistance are equally subject to the disclosure requirement.
In sum, this narrowly focused bill does not regulate AI development, research, or commercial deployment beyond printed/digital “books.” It creates a single, mandatory transparency requirement, but leaves key terms and enforcement wholly undefined.
Assembly - 1952 - Requires employers and employment agencies to notify candidates for employment if machine learning technology is used to make hiring decisions
Legislation ID: 55729
Bill URL: View Bill
Sponsors
Assembly - 222 - Relates to liability for misleading, incorrect, contradictory or harmful information provided to a user by a chatbot
Legislation ID: 53999
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of A.222-A (2025-2026), laid out in the five requested sections. Every claim is tied directly to bill text.
Section A: Definitions & Scope
––––––––––––––––––––––––
1. “Artificial intelligence” (§390-f.1(a))
– Text: “Artificial intelligence means a machine-based system … that … infers … how to generate outputs such as predictions, content, recommendations, or decisions…”
– Relevance: This broad definition explicitly targets AI systems capable of learning or inference. It covers classical ML, generative models, expert systems, etc.
2. “Chatbot” (§390-f.1(b))
– Text: “Chatbot means an artificial intelligence system … that simulates human-like conversation … to provide information and services to users.”
– Relevance: Narrows in on AI deployments that converse with users. All subsequent liability rules apply only to these AI-driven conversants.
3. “Companion chatbot” (§390-f.1(c))
– Text: “Companion chatbot means a chatbot … designed to provide human-like interaction that simulates an interpersonal relationship … uses previous user interactions …”
– Relevance: Defines the subset of chatbots with ongoing personal/therapeutic relationships. The bill imposes extra self-harm and minor protections on these.
4. “Human-like” (§390-f.1(e))
– Text: “Human-like means any form of communication or interaction that approximates human behavior …”
– Relevance: Ensures wide capture of AI that mimics people, role-plays, or otherwise masks its non-human nature.
5. “Proprietor” (§390-f.1(g))
– Text: “Proprietor means any person, business … that owns, operates or deploys a chatbot … Proprietors shall not include third-party developers that license their technology.”
– Relevance: Targets entities commercializing AI chatbots, not the underlying AI libraries or platforms.
Section B: Development & Research
–––––––––––––––––––––––
This bill contains no provisions on AI R&D (e.g., no funding mandates, reporting, or data-sharing rules). Its focus is entirely on deployed, user-facing chatbot products.
Section C: Deployment & Compliance
––––––––––––––––––––––––––––
1. Liability for misleading or harmful chatbot output (§390-f.2)
– Text (§2(a)): “A proprietor … may not disclaim liability where a chatbot provides materially misleading, incorrect, contradictory or harmful information … that results in financial loss or other demonstrable harm …”
– Impact: Proprietors must insure or limit risk of bad advice. May slow experimental features or raise insurance costs for startups.
2. Accuracy aligned with policies (§390-f.2(b))
– Text: “The proprietor … shall be responsible for ensuring such chatbot accurately provides information aligned with the formal policies, product details, disclosures and terms of service …”
– Impact: Requires compliance pipelines, human review, ongoing model validation.
3. No blanket disclaimer (§390-f.2(c))
– Text: “A proprietor may not waive or disclaim this liability merely by notifying consumers that they are interacting with a non-human chatbot system.”
– Impact: “We’re just a bot” label won’t shield against suit. Encourages higher guardrails.
4. Mandatory disclosure (§390-f.4)
– Text: “Proprietors … shall provide clear, conspicuous and explicit notice … that they are interacting with an artificial intelligence chatbot … in a size no smaller than the largest font size of other text.”
– Impact: UI requirements; minor but enforceable design rule.
5. Self-harm safeguards for companion chatbots (§390-f.5)
– Text (§5(a)): “A proprietor … shall use commercially reasonable and technically feasible methods to (i) prevent … self-harm, and (ii) determine whether a covered user is expressing thoughts of self-harm and … prohibit continued use for at least twenty-four hours and prominently display a means to contact a suicide crisis organization.”
– Impact: Forces mental-health AI to integrate self-harm detection and crisis-signposting. Smaller players may struggle with the technical feasibility of reliable detection.
6. Minor protection (§390-f.6)
– Text (§6(a)): “A proprietor … shall use commercially reasonable and technically feasible methods to determine whether a covered user is a minor.”
– Text (§6(b)(i)): “cease such covered user’s use … until … verifiable parental consent.”
– Impact: Brings COPPA-like obligations to chatbots. May chill open access to conversational products that don’t have age-gate systems.
7. System vulnerability checks (§390-f.7)
– Text: “A proprietor … shall implement … methods to discover vulnerabilities in the proprietor’s system, including any methods used to determine whether a covered user is a minor.”
– Impact: Encourages regular security audits. Compliance cost, but fosters safer deployments.
8. Attorney General rulemaking (§390-f.8, §390-f.10)
– Text (§8(a)): “The attorney general shall promulgate regulations identifying commercially reasonable and technically feasible methods …”
– Impact: Future AG regulations will fill in details on age-verification, self-harm detection accuracy thresholds, etc. Might evolve with technology.
Section D: Enforcement & Penalties
––––––––––––––––––––––––––––
1. Private liability
– False info (§390-f.2, §390-f.3): Users harmed by bad financial or bodily-harm advice have claim against the proprietor. Companion chatbot self-harm claims (§390-f.5(b,c)) and minor self-harm strict liability (§390-f.6(c)).
– These sections provide civil causes of action; no monetary caps are specified.
2. Non-waivable rights
– Text (§2(c), §5(d), §6(d)): “A proprietor may not waive or disclaim liability … under this subdivision.”
– Implication: End-users cannot contract away these protections.
3. AG oversight
– AG promulgation of regs (§390-f.8, §390-f.10) and enforcement of compliance with disclosure, vulnerability, self-harm and minor safeguards. Violations may trigger AG enforcement powers under GBL.
4. No criminal sanctions or fines specified
– The bill relies on private suits and AG regulation enforcement; there are no explicit monetary penalties or criminal liability spelled out.
Section E: Overall Implications
––––––––––––––––––––––––––––
• Compliance Burden: Even early-stage AI startups offering chatbots will need:
– age-gate and parental-consent flows,
– self-harm detection and crisis referrals,
– factual accuracy audits,
– prominent AI disclosure labels,
– regular security and bias vulnerability scans.
• Liability Risk: Strict liability for minors’ self-harm and bodily-harm advice will drive up insurance costs or deter novel “companion” products.
• Consumer Safety vs. Innovation Trade-off:
– Pros: Likely reduces misinformation, unsafe suggestions, and protects minors.
– Cons: High cost of compliance may concentrate the market among large incumbents able to absorb legal risk and invest in robust detection pipelines.
• Regulatory Uncertainty: The AG’s forthcoming “commercially reasonable and technically feasible” standards (§390-f.8) will shape final compliance requirements and could evolve rapidly alongside AI capabilities.
• No R&D Support: The bill imposes restrictions but offers no incentives or support for AI research, suggesting a purely protective/regulatory posture.
In sum, A.222-A squarely targets AI chatbots—especially “companion” systems—by defining them, imposing non-waivable consumer protection obligations, and creating liability for harmful outputs. It stops short of funding or encouraging AI development, instead aiming to guard New Yorkers against misleading or dangerous AI-driven conversations.
Assembly - 235 - Relates to unauthorized depictions of public officials generated by artificial intelligence
Legislation ID: 54012
Bill URL: View Bill
Sponsors
Assembly - 3125 - Relates to the use of automated decision tools by landlords for making housing decisions
Legislation ID: 57616
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of Assembly Bill A.3125 (the “bill”), organized in the five sections you requested. All quotations cite section, paragraph, and line ranges from the bill text.
Section A: Definitions & Scope
1. “Automated decision tool” (AI-related definition)
• Quotation: “’Automated decision tool’ means any computational process, derived from machine learning, statistical modeling, data analytics, or artificial intelligence, that issues simplified output… that is used to substantially assist or replace discretionary decision making for making housing decisions that impact natural persons.” (§227-g(1)(a), lines 5–11)
• Analysis: By explicitly naming “machine learning,” “statistical modeling,” “data analytics,” and “artificial intelligence,” the bill targets a broad set of AI-powered systems used to screen housing applicants. It thus covers both classical algorithmic credit-scoring tools and modern ML-based risk predictors.
2. Exclusion of non-AI tools
• Quotation: “’Automated decision tool’ does not include a tool that does not automate… discretionary decision-making processes and that does not materially impact natural persons, including, but not limited to, a junk email filter, firewall, antivirus software, calculator, spreadsheet, database….” (§227-g(1)(a), lines 11–15)
• Analysis: The bill draws a bright line excluding generic IT utilities (e.g. spreadsheets or firewalls). This narrows the compliance burden strictly to systems that produce a “score, classification, or recommendation.”
3. “Disparate impact analysis”
• Quotation: “’Disparate impact analysis’ means an impartial evaluation conducted by an independent auditor… testing of the extent to which use of an automated decision tool is likely to result in an adverse impact to the detriment of any group on the basis of sex, race, ethnicity, or other protected class…” (§227-g(1)(b), lines 16–23)
• Analysis: The definition explicitly embeds a fairness/audit requirement—central to responsible AI practice—to detect bias against protected groups.
4. “Housing decision”
• Quotation: “’Housing decision’ means to screen applicants for housing.” (§227-g(1)(c), line 24)
• Analysis: This ensures the bill focuses purely on tenant-screening scenarios and does not regulate other real-estate or credit uses of AI.
Section B: Development & Research
No clauses in this bill directly address AI R&D funding, university research, data-sharing mandates, or innovation incentives. The primary focus is on operational use of AI tools by landlords, not on development pipelines or research institutions.
Section C: Deployment & Compliance
1. Annual bias audit requirement
• Quotation: “No less than annually, a disparate impact analysis shall be conducted to assess the actual impact of any automated decision tool used by any landlord to select applicants for housing within the state.” (§227-g(2)(a), lines 3–7)
• Analysis: This imposes a recurring compliance cost on any entity deploying an AI screening tool. Startups and small landlords may face challenges hiring independent auditors for ML bias testing.
2. Public disclosure of audit summary
• Quotation: “A summary of the most recent disparate impact analysis… shall be made publicly available on the website of the landlord… and… through any listing for housing on a digital platform…” (§227-g(2)(b), lines 8–13)
• Analysis: This transparency mandate pressures landlords (and by extension, their AI-vendor partners) to surface bias-testing results to prospective tenants. This could drive broader industry standardization of audit reporting formats.
3. Applicant notice and explanation rights
• Quotation:
– “Any landlord… shall notify each such applicant of the following: (i) That an automated decision tool will be used…; (ii) The characteristics that such automated decision tool will use…; (iii) Information about the type of data collected…, the source…, and the landlord’s data retention policy; and (iv) If an application… is denied… the reason for such denial.” (§227-g(3)(a), lines 14–25)
– “The notice… shall be made no less than twenty-four hours before the use of such automated decision tool and shall allow such applicant to request an alternative selection process or accommodation.” (§227-g(3)(b), lines 26–29)
• Analysis: This mirrors explainability and contestability requirements in AI governance frameworks (e.g., EU AI Act). It forces AI vendors to bake in user-facing explainers and appeals workflows—raising development overhead but improving end-user transparency.
Section D: Enforcement & Penalties
1. Attorney General investigation authority
• Quotation: “The attorney general may initiate an investigation if a preponderance of the evidence, including the summary of the most recent disparate impact analysis establishes a suspicion of a violation.” (§227-g(4), lines 30–33)
• Analysis: This gives the NY AG broad discretion to probe suspected non-compliance. It aligns with existing civil rights enforcement but extends to algorithmic bias.
2. Court actions and injunctive relief
• Quotation: “The attorney general may also initiate… any action… for correction of any violation… including mandating compliance… or such other relief as may be appropriate.” (§227-g(4), lines 33–37)
• Analysis: Non-compliant landlords (or their AI tool vendors, indirectly) may face injunctions, compliance mandates, and potentially damages or stipulated penalties—incentivizing pre-deployment compliance reviews.
Section E: Overall Implications
1. Advancement of responsible AI
– By codifying annual audits, public reporting, explainability notices, and appeal rights, the bill institutionalizes several best practices from the AI fairness and transparency communities. This could accelerate vendor adoption of audit toolkits (e.g. IBM AI Fairness 360, Google’s What-If Tool).
2. Increased compliance costs
– Small landlords and emerging AI startups may struggle with the recurring cost of third-party audits and public-report disclosures. This could consolidate the market around established screening-software vendors with in-house compliance teams.
3. Market reshaping
– Transparency requirements may push housing platforms to differentiate on “fairness certified” AI tools, creating a market premium for audited, explainable systems. Some landlords may revert to manual processing to avoid compliance burdens.
4. Regulatory clarity vs. ambiguity
– Ambiguity: The bill does not specify “acceptable” disparate impact thresholds or detailed audit methodologies, leaving these to regulation or litigation. Vendors may need to seek legal guidance or wait for AG policy statements.
– Interpretation: The open-ended “other relief” clause (§227-g(4), lines 36–37) could result in varying enforcement outcomes (warnings, fines, remedial training).
In sum, A.3125 explicitly targets AI-powered tenant-screening tools, imposing transparency, audit, and due-process safeguards. Its strongest impacts will be felt in deployment and compliance, steering landlords and AI vendors toward standardized fairness-testing and explainability practices—at the cost of higher operational overhead and some regulatory uncertainty.
Assembly - 3265 - Enacts the New York artificial intelligence bill of rights
Legislation ID: 57911
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. “Automated system” (Sec. 501.5, lines 3–10)
Definition: “any system, software, or process that … uses computation … to determine outcomes, make or aid decisions, inform policy implementation, collect data or observations, or otherwise interact with New York residents or communities.”
AI Relevance: This explicitly covers AI/ML/statistical systems (“derived from machine learning, statistics, or other data processing or artificial intelligence techniques”).
Scope Impact: Startups and vendors will know that any AI-driven product falls under this law. Researchers must flag work as “automated system” for compliance.
2. “Passive computing infrastructure” (Sec. 501.6, lines 11–15)
Definition: “intermediary technology that does not influence or determine the outcome of decisions … excluding web hosting, domain registration, … data storage, or cybersecurity.”
AI Relevance: Separates core AI systems from mere hosting or networking—limiting compliance burden on infrastructure providers.
3. “Sensitive data” & “Sensitive domain” (Sec. 501.11–12, lines 36–53)
Definition: Data about protected traits or likely to cause harm; domains where “activities … can cause material harms.”
AI Relevance: Requires stronger data safeguards and review for AI systems handling e.g. health, criminal justice.
4. Application (Sec. 502, lines 4–10)
“All rights … apply … against persons developing automated systems that … impact New York residents’ civil rights…, equal opportunities, or access to critical resources.”
AI Relevance: Clarifies that any AI system affecting rights, opportunities, or services falls into regulatory scope.
Section B: Development & Research
1. Pre-deployment testing, risk identification, and mitigation (Sec. 504.2, lines 18–22)
Quote: “Automated systems shall undergo pre-deployment testing, risk identification and mitigation, and … ongoing monitoring that demonstrates they are safe and effective…”
Impact: Research projects and pilots in universities or startups must include formal risk assessments before deployment. May slow early-stage experimentation.
2. Collaboration and stakeholder input (Sec. 504.1, lines 13–17)
Quote: “These systems must be developed in collaboration with diverse communities, stakeholders, and domain experts…”
Impact: Encourages participatory research; may impose new procedures for grant-funded AI studies to document community engagement.
3. Independent evaluation & public reporting (Sec. 504.6, lines 34–37)
Quote: “Independent evaluation and reporting that confirms that the system is safe and effective … shall be performed and the results made public whenever possible.”
Impact: Researchers and companies need to budget for independent audits and possibly redacted public disclosures.
Section C: Deployment & Compliance
1. Algorithmic discrimination safeguards (Sec. 505.2–5, lines 41–54)
Quote: “Designers … shall take proactive … measures … including proactive equity assessments … representative data, protection against proxies … accessibility … pre-deployment and ongoing disparity testing and mitigation … independent evaluations … algorithmic impact assessment.”
Impact: Vendors must integrate fairness toolkits and conduct continuous disparity audits; potential liability if neglected.
2. Data privacy by design (Sec. 506.2–6, lines 6–24)
Quote: “Privacy protections by default … only strictly necessary data … consent … brief, understandable … any existing practice of complex notice-and-choice … transformed.”
Impact: AI products must default to minimal data collection; UI/UX redesigns for consent flows. End-users gain clearer choices.
3. Surveillance constraints (Sec. 506.8–9, lines 30–37)
Quote: “Surveillance technologies shall be subject to heightened oversight … pre-deployment assessment … Continuous surveillance … shall not be used in education, work, housing…”
Impact: Government or commercial AI surveillance must undergo extra review; some products (e.g. proctoring software) may be banned in certain contexts.
4. Notice & explanation (Sec. 507.1–5, lines 42–53)
Quote: “Informed when an automated system is in use … accessible plain language documentation … how and why … explanations … proportionate to the level of risk.”
Impact: AI vendors need to supply clear customer-facing documentation and individual explanations. Could drive new explanation-as-a-service offerings.
5. Human alternative & fallback (Sec. 508.1–4, lines 11–24)
Quote: “Right to opt out of automated systems … in favor of a human alternative … human consideration and remedy … fallback … accessible, equitable, and effective.”
Impact: Customer support and case-handling workflows must include human agents; some fully automated services may not meet these requirements.
Section D: Enforcement & Penalties
1. Civil penalties by Attorney General only (Sec. 509.1–2, lines 36–42)
Quote: “Operator … shall be liable … for a penalty not less than three times such damages … recovered by an action brought by the attorney general …”
Impact: No private lawsuits; centralized enforcement likely through State AG—vendors face treble damages if penalized.
2. No private cause of action (Sec. 509.3, lines 43–46)
Quote: “Nothing … shall be construed as creating … a private cause of action by an aggrieved person…”
Impact: Individual end-users cannot sue directly; may limit frivolous claims but places burden on public enforcer.
Section E: Overall Implications
1. Reshaping AI deployment: The bill establishes rigorous pre-deployment testing, fairness audits, and transparency obligations (Secs 504–507), likely raising compliance costs and complexity for developers and vendors.
2. Promoting trustworthy AI: By codifying the White House “Blueprint for an AI Bill of Rights” (Leg. Intent, lines 8–14), the state signals a commitment to ethical AI, encouraging “privacy by design” and “AI explainability” practices.
3. Balancing innovation and regulation: Exempting passive infrastructure (Sec. 501.6) and centralizing enforcement (Sec. 509) eases burdens on non-AI providers and limits litigation risks, while robust rights protect residents.
4. Ambiguities and interpretations: Phrases like “meaningful oversight” (Sec. 501.1) and “when appropriate” for opt-out rights (Sec. 508.1) lack precise metrics—regulators will need to define standards and thresholds, affecting ease of compliance.
Assembly - 3327 - Relates to political communication utilizing artificial intelligence
Legislation ID: 58041
Bill URL: View Bill
Sponsors
Assembly - 3356 - Relates to enacting the "advanced artificial intelligence licensing act"
Legislation ID: 58101
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of the proposed “Advanced Artificial Intelligence Licensing Act,” organized into five sections. All quotations cite bill text (article/section and subdivision or paragraph).
Section A. Definitions & Scope
1. “Advanced artificial intelligence system” (§ 501.1)
– “any digital application or software … that autonomously performs functions traditionally requiring human intelligence. This includes … (a) Having the ability to learn from and adapt …; or (b) Having the ability to perform functions that require cognitive processes …”
– Relevance: This catch-all definition explicitly targets modern machine-learning and decision-making systems.
2. “High-risk advanced artificial intelligence system” (§ 501.2)
– “any advanced artificial intelligence system that possesses capabilities that can cause significant harm … to liberty, emotional, psychological, financial, physical, or privacy interests … or which have significant implications on governance, infrastructure, or the environment.”
– Relevance: Draws a regulatory line around AI applications deemed to pose serious risks (e.g., healthcare diagnosis, autonomous vehicles, surveillance, weapons).
3. “Uncontained” (§ 501.4)
– “critical components of the source code … reproduced by an amount of individuals so numerous that it is … practically impossible to prohibit or control its usage.”
– Relevance: Seeks to capture viral open-source AI models once widely shared, triggering extra controls.
4. “Operator” (§ 501.5) and “Publicly accessible code” (§ 501.6)
– Define who must register/license (the “operator” distributing or controlling an AI system, including hosting platforms).
5. New “Log(s)” (§ Part B, amending § 501)
– “systematic, chronologically ordered record of events pertaining to a system’s operations, activities, and transactions …”
– Relevance: Introduces mandatory logging of AI behavior for audit and compliance.
Section B. Development & Research
1. Duty to register (§ 510)
– “Any person who develops a high-risk … system … shall have the duty to disclose … by applying for a license … upon active deployment.”
– (§ 510.1)
– Impact: Forces researchers and startups to notify the state once their prototypes cross into “high-risk” territory, potentially slowing confidentiality and open-inquiry.
2. Pre-development notice for neural-decoding AI (§ 510.2)
– “Any person developing a system … described in paragraph (i) of subdivision 2 of section 501 … shall disclose … prior to active development … The secretary may … require such person to cease development … where … the system has a high likelihood of violating § 529 or § 530.”
– Impact: Imposes an early-stage “stop-and-check” for brain-computer-interface and other sensitive AI R&D, chilling certain pioneering work.
3. Advisory Council (§ Part A, § 504–§ 505)
– Brings together industry appointees, agency heads, and the secretary for “review and comment on all rules” (§ 505.1) and “non-binding recommendations” on licensing (§ 505.2).
– Impact: Creates a multi-stakeholder body to advise on research directions and risk thresholds—may provide industry advocates a seat at the table but could also slow rulemaking.
4. Source-code review (§ 517)
– “Secretary shall conduct periodic evaluations of the source code and outcomes …”
– (§ 517.1–3)
– Impact: Researchers wishing to update or rewrite code must submit plans and wait up to 180 business days (§ 519.3), hindering agile experimentation.
Section C. Deployment & Compliance
1. Licensing requirement (§ 511)
– “No person shall … develop … or operate a high-risk … system … without first obtaining a license.”
– (§ 511.1)
– Impact: Treats AI like a regulated utility; established vendors must budget for application and renewal fees (adjusted by “size of the business” and “risk” (§ 507)), while startups face barriers to market entry.
2. Supplemental licenses (§ 512)
– “Where a person … is licensed … such person shall apply for a supplemental license for each additional high-risk … system …”
– Impact: Discourages development of multiple AI products under one umbrella, increasing administrative overhead for growth.
3. Ethics & Risk Management Board (§ 516)
– “Every operator … shall establish an ethics and risk management board … to assess … all possible use cases … and current operational outcomes.” (§ 516.1–4)
– Impact: Requires firms to staff independent oversight bodies and file annual risk reports, adding governance costs but promoting responsible deployment.
4. Logging (§ 524) and Internal Controls (§ 525)
– Mandates logs that “preserved for ten years” (§ 524) and “internal controls that … can safely and indefinitely cease operation” (§ 525).
– Impact: Raises compliance and cybersecurity costs; users may welcome “kill-switch” safety measures.
5. Third-party integration (§ 523)
– Non-licensed software may integrate if it obtains a “certificate of compliance” with state cybersecurity standards.
– Impact: Encourages a local ecosystem of vetted plug-ins but may deter small vendors from integrating.
6. Code modifications, updates, rewrites (§ 519)
– Licensees must notify the secretary of “modifications or upgrades,” await approval (30 business days, then auto-approve), and face a 180-day review for “rewrites.”
– Impact: Slows product iterations, discourages continuous improvement unless regulators expedite reviews.
Section D. Enforcement & Penalties
1. Department powers (§ 502)
– Can “issue or refuse … licenses” (§ 502.1), “subpoena witnesses … examine … production of … source code or logs” (§ 502.5), and “impose civil or criminal penalties” (§ 502.3).
2. Civil penalties (§ 508)
– Violations carry civil fines “not to exceed the amount gained from such violation or the actual damages caused, whichever is greater” (§ 508.1).
3. Criminal offenses
– False statements in applications: misdemeanor, up to $500 fine or six months jail (§ 508.5).
– Uncontained high-risk code:
• Willful uncontainment: class E felony (§ 518.2).
• Negligent uncontainment: class A misdemeanor (§ 518.3).
• Uncontainment of especially dangerous systems (paragraph (f) or prohibited under § 530): class C felony (§ 518.4).
4. Prohibited systems (§ 530)
– Deploying subliminal manipulation (§ 530.1(a)), unauthorized data exfiltration (§ 530.1(d)), or “autonomous weapon systems … that lack meaningful human supervision” (§ 530.1(e)) is banned. Violation = class D felony + civil penalty “amount earned or damages caused, whichever is greater” (§ 530.4).
5. Emergency suspensions
– Secretary may “without notice and a hearing, suspend any license … for … up to thirty days” if “substantial risk of public harm” (§ 515.5).
Section E. Overall Implications
– Innovation vs. Oversight: The bill creates a comprehensive licensing regime that treats advanced AI like a highly-regulated industry (e.g., pharmaceuticals or aviation). Reporting, boards, source-code reviews, and fees will raise costs and slow time-to-market, favoring well-capitalized incumbents over small startups and university labs.
– Safety & Accountability: Mandatory ethics boards (§ 516), logging (§ 524), kill switches (§ 525), and criminal penalties for uncontained code (§ 518) push firms to prioritize reliability and governance.
– Chilling Effect on Research: Pre-development notices (§ 510.2) for neural decoding and protracted rewrites reviews (§ 519) risk discouraging cutting-edge work in brain-machine interfaces, explainable AI, and open-source innovation.
– Regulatory Flexibility: The advisory council (§ 504–505) and broad rulemaking authority (§ 506, § 521) give regulators discretion to refine obligations over time but may also lead to uncertainty about compliance expectations.
– Ecosystem Shifts: End-users gain more transparency and potential recourse (e.g., logs, kill switches), while vendors must navigate a fortress of licensing, reporting, and penalties, reshaping New York into a highly supervised AI jurisdiction.
Assembly - 3361 - Creates a temporary state commission to study and investigate how to regulate artificial intelligence, robotics and automation
Legislation ID: 58110
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of Assembly Bill A.3361 (2025-2026), organized in the sections you requested. Every point is tied to exact bill language; where the bill is silent or ambiguous, I note that as well.
Section A: Definitions & Scope
1. No explicit “definitions” clause
– The bill does not define terms such as “artificial intelligence,” “robotics,” or “automation.” This omission means the commission (and later lawmakers relying on its report) will need to choose or craft working definitions.
– Ambiguity impact: Without definitions, stakeholders may interpret the scope more narrowly (e.g., only “machine learning” under AI) or more broadly (e.g., including any software automation).
2. Scope statements targeting AI
– Section 1 enumerates the commission’s study topics; each sub-paragraph ties back to AI, robotics or automation:
• (a) “current law within this state addressing artificial intelligence, robotics and automation”
• (b) “comparative state policies … for artificial intelligence, robotics and automation”
• (c) “criminal and civil liability regarding violations of law caused by entities equipped with artificial intelligence, robotics and automation”
• (d) “the impact of artificial intelligence, robotics and automation on employment in this state”
• (e) “the impact … on the acquiring and disclosure of confidential information”
• (f) “potential restrictions on the use of artificial intelligence, robotics and automation in weaponry”
• (g) “the potential impact on the technology industry of any regulatory measures proposed by this study”
• (h) “public sector applications of artificial intelligence and cognitive technologies”
Citation: Bill, § 1, lines 4–21.
Section B: Development & Research
The bill does not include any provisions that directly mandate or fund AI R&D, nor does it impose reporting requirements on researchers or data-sharing rules. Its primary R&D implication is through the commission’s recommendations.
– Potential indirect effects:
• If the commission’s final report (due by Dec. 1, 2026) recommends state research grants, procurement preferences, or data-sharing frameworks, that could reshape university and startup research.
• But as written, there are no immediate mandates or processes for data sharing or academic-industry collaboration.
Citation: Review of entire bill—no text in §§ 2–6 pertains to funding mandates, data sharing, or direct R&D obligations.
Section C: Deployment & Compliance
Again, the bill does not itself impose compliance requirements on AI products or services. Instead, it tasks the commission with studying:
– Liability frameworks (§ 1(c)): “criminal and civil liability regarding violations of law caused by entities equipped with artificial intelligence, robotics and automation”
• Potential impact: The commission could later recommend new product-liability rules for AI, or modifications to existing negligence/strict liability principles.
– Confidential information (§ 1(e)): “the impact of artificial intelligence, robotics and automation on the acquiring and disclosure of confidential information”
• Potential impact: Recommendations might include new privacy audits or transparency requirements for AI systems that ingest personal data.
– Weaponry (§ 1(f)): “potential restrictions on the use of artificial intelligence, robotics and automation in weaponry”
• Potential impact: Future regulations on autonomous drones or lethal autonomous weapons systems might stem from this study.
But no current deployment or certification rules are imposed by this text.
Section D: Enforcement & Penalties
There are no enforcement mechanisms or penalties in the bill itself:
– Commission members are unpaid (§ 3) and serve “at the pleasure of the official making the appointment” (§ 2).
– The act “shall expire and be deemed repealed December 31, 2026” (§ 6).
– No fines, injunctions, or criminal penalties are created. Enforcement will depend on the commission’s recommendations, if adopted later.
Section E: Overall Implications
1. Establishes a forum for multi-stakeholder AI policy development
– By including appointees from the Governor’s office, legislative leaders, the Attorney General, and SUNY/CUNY chancellors (§ 2), the commission is designed to balance industry, legal, academic, and political perspectives.
2. Sets a 2-year window for study and reporting
– Final report due “no later than thirty days prior to the expiration of this act” (i.e., by Nov. 30, 2026) (§ 5).
– This timeline pressures the commission to deliver actionable policy recommendations quickly, but two years may also be too short for deep technical and economic analysis.
3. Leaves key definitions and scope broad and ambiguous
– By not defining “AI,” “robotics,” or “automation,” the bill delegates definitional choices to the commission. This could lead to widely varying regulatory proposals depending on appointees’ technical backgrounds.
4. Impacts on stakeholders
– Researchers and universities: Limited immediate effect, though CUNY/SUNY representation could shape research-oriented recommendations.
– Startups and established vendors: No direct compliance costs now, but uncertainty may slow investment until policy direction is clear.
– Regulators and legislators: Commission report will serve as the first comprehensive state-level blueprint for AI regulation in New York.
– End users and civil-rights groups: Section 1(e) on confidential information signals potential future privacy and transparency protections.
In sum, A.3361 does not itself regulate AI products or R&D. Rather, it creates a time-limited, high-level body charged with studying the legal, economic, and ethical challenges of AI, robotics, and automation. The true regulatory and practical impacts will depend on the content of the commission’s report and any subsequent legislation or executive action that follows.
Assembly - 3411 - Requires warnings on generative artificial intelligence systems
Legislation ID: 58208
Bill URL: View Bill
Sponsors
Assembly - 3719 - Enacts the robot tax act; imposes a tax on certain businesses when people are displaced from their employment due to certain technologies
Legislation ID: 58863
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused read of A.3719 (“Robot Tax Act”), with each citation anchored to the bill text. Because this bill lives entirely in the State Tax Law rather than a standalone AI-regulatory code, most of its “AI provisions” are located in definitions (§ 209-A(1)(a)) and in the surcharge triggered by worker displacement (§ 209-A(1)(a)).
Section A: Definitions & Scope
1. Definition of “technology” (and AI)
• Text: “For the purposes of this section, the term ‘technology’ shall include, but not be limited to, machinery, artificial intelligence algorithms, or computer applications.”
– Citation: § 209-A(1)(a), lines 23–24.
– Analysis: This clause explicitly brings AI under the surcharge regime by naming “artificial intelligence algorithms.” It is the sole place in the bill where AI is singled out—everything else uses the umbrella term “technology.”
2. Definition of “employee … displaced … by technology”
• Text: surcharge applies “for an employee’s final year of employment with the company where such employee was displaced in such taxable year due to the employee’s position being replaced by technology.”
– Citation: § 209-A(1)(a), lines 19–22.
– Analysis: AI systems that replace human roles trigger the surcharge. Ambiguity remains around how “displaced” is proved—e.g., must a company record that a specific AI rollout directly caused the layoff? The bill does not spell out evidentiary requirements.
3. Thresholds of “deriving receipts from activity in this state”
• Text: “A corporation is deriving receipts from activity in this state if it has receipts within this state of one million dollars or more in the taxable year.”
– Citation: § 209-A(1)(b), lines 25–31.
– Analysis: Large multinationals deploying AI at scale in New York will almost certainly exceed $1 million annual in-state receipts—this targets big tech more than small AI startups.
4. Thresholds of “doing business in this state”
• Text (selected): “(i) it has issued credit cards to one thousand or more customers who have a mailing address within this state … (ii) it has merchant customer contracts … total number of locations … one thousand or more … (iii) sum of (i) and (ii) equals one thousand or more.”
– Citation: § 209-A(1)(c), lines 7–16.
– Analysis: These bright-line tests are carried over from existing tax law and ensure that AI vendors with large user bases or merchant partnerships fall within scope.
Section B: Development & Research
This bill does not address AI research funding, data-sharing, or reporting requirements. Its focus is entirely on taxing deployment that displaces workers.
Section C: Deployment & Compliance
1. Surcharge formula
• Text: surcharge “in an amount equal to the sum of any taxes or fees imposed by the state or any political subdivision thereof computed based on an employee’s wage … for an employee’s final year of employment … displaced … by technology.”
– Citation: § 209-A(1)(a), lines 15–22.
– Analysis: Companies must calculate, for each displaced worker, the total state income taxes, unemployment insurance, local occupational taxes paid by employer or employee in that final year—and then pay the same amount again as a “robot surcharge.” This is a novel compliance requirement:
– Established vendors deploying AI at scale must add this line item to their quarterly/annual tax filings.
– Startups that displace even a handful of employees will face an outsized marginal tax if those employees had high wages or costly benefits.
2. Applicability to partnerships and unitary groups
• Text (partnerships): “If a partnership is … doing business … in this state … any corporation that is a partner … shall be subject to tax under this article … .”
– Citation: § 209-A(1)(f), lines 50–54.
• Text (unitary groups): paragraphs (d)(i)–(ii) set thresholds for small affiliates of large multinational groups.
– Citation: § 209-A(1)(d), lines 19–36.
– Analysis: This closes a potential loophole by pulling in corporations that replace in-state workers via an AI rollout in a partnership or through an affiliate in a unitary group.
Section D: Enforcement & Penalties
1. Enforcement mechanism
• Text: “For the privilege of exercising its corporate franchise … there is hereby imposed … a tax surcharge …”
– Citation: § 209-A(1)(a), lines 6–13.
– Analysis: The State Tax Department will collect the surcharge through standard corporate franchise tax returns. No new enforcement body or audits specific to AI are created—auditors will simply ask for documentation of “displaced” employees and verify the surcharge.
2. Effective date and rule-making
• Text: “This act shall take effect immediately and shall apply to taxable years starting January 1, 2026. Effective immediately, the addition, amendment and/or repeal of any rule or regulation necessary … are authorized to be made … on or before such effective date.”
– Citation: § 3, lines 49–53.
– Analysis: The Department must promulgate guidance on identifying displaced employees, calculating the surcharge, and resolving ambiguities (e.g., shared displacement caused by both AI and other cost-cutting).
Section E: Overall Implications
1. Disincentive for AI-driven automation
– By equating the surcharge to all state and local employment taxes for each displaced worker, the bill creates a steep marginal cost on replacing humans with AI. Companies will weigh that surcharge against projected savings from automation.
2. Impact on research and startups
– Because R&D activities are exempt—as long as they do not yet displace workers—labs and universities face no immediate impact. However, startups that scale rapidly and begin replacing staff may reach the $1 million-receipt threshold quickly and incur the surcharge.
3. Effect on established vendors
– Large tech firms with broad deployment of AI in New York are prime targets. They will need to track every automation-driven layoff, calculate state/local tax equivalents, and budget accordingly.
4. State-level policy signal
– This is one of the first U.S. “robot tax” proposals to specifically call out AI algorithms. It signals legislative interest in slowing workforce displacement, although it provides no direct protections or retraining funds.
Ambiguities to resolve in rule-making:
• What documentation suffices to prove that a specific AI system, rather than outsourcing or economic downturn, caused an employee’s displacement?
• How are partial displacements handled (e.g., a worker whose hours are cut due to AI-assisted efficiency)?
• Will the surcharge apply to positions never backfilled, or only to expressly automated headcount reductions?
In sum, A.3719 uses the tax code to target AI-driven worker displacement. Its explicit mention of “artificial intelligence algorithms” (§ 209-A(1)(a)) inscribes AI at the core of a new “robot tax,” shifting part of the social cost of automation back onto firms that pursue it.
Assembly - 3779 - Relates to restricting the use of electronic monitoring and automated employment decision tools
Legislation ID: 58991
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an analysis of Assembly Bill A.3779 (“Boundaries on Technology Act”), organized in the five sections you requested. Wherever possible, I quote directly from the bill text (section and paragraph numbers) to support each point.
Section A: Definitions & Scope
1. “Automated employment decision tool” (1010-2)
• Language: “any computational process, automated system, or algorithm derived from machine learning, statistical modeling, data analytics, artificial intelligence, or similar methods that issues an output…used to assist or replace human decision making on employment decisions.”
• Relevance to AI: Explicitly names “machine learning,” “statistical modeling,” “data analytics,” and “artificial intelligence” as covered techniques.
• Scope: Applies to any software or algorithmic system that evaluates, ranks, or scores employees or candidates.
2. “Impact Assessment” and “Impartial Auditor” (1010-8, 9)
• Language: “‘Impact assessment’ means an evaluation by an impartial auditor…of an automated employment decision tool.”
• Relevance: Mandates third-party technical evaluations of AI/ML systems for fairness and bias.
3. “Vendor” (1010-11)
• Language: “any person or entity who sells, distributes, or develops for sale an automated employment decision tool…”
• Scope: Includes downstream providers of AI systems, not just end-user employers.
4. “Protected class” (1010-10)
• Language: “a class enumerated in section two hundred ninety-six of the executive law.”
• Relevance: Ties AI fairness requirements to existing civil-rights categories (race, gender, disability, etc.).
Section B: Development & Research
The bill does not contain any provisions specifically funding or mandating AI research or data-sharing for R&D. Instead, it focuses on post-development evaluation and use in employment contexts. There are no reporting requirements or data-sharing obligations aimed at facilitating research; rather, all data-sharing is framed around impact assessments (see Section A).
Section C: Deployment & Compliance
1. Mandatory Impact Assessments (1011-1)
• “It shall be unlawful for an employer with one hundred or more employees to use an automated employment decision tool…unless such tool has been the subject of an impact assessment.”
• Requirements include disclosure of “attributes and modeling techniques” (1011-1(c)), analysis of “disparate impact on persons belonging to a protected class” (1011-1(d)), and identification of “least discriminatory method” (1011-1(g)).
2. Ongoing Monitoring (1011-2)
• Language: “An employer…shall conduct or commission subsequent impact assessments each year that the tool is in use.”
• Effect: Imposes a recurring audit cycle on deployed AI systems.
3. Data Privacy During Audits (1011-4)
• Language: “Employee data…shall be collected, processed, stored, and retained in such a manner as to protect the privacy of employees.”
• Relevance: AI deployments often require large datasets; this clause constrains how that data may be shared with auditors.
4. Human-in-the-Loop Requirement (1012-2(b))
• Language: “An employer shall not solely rely on output from an automated employment decision tool when making hiring, promotion…decisions. An employer shall establish meaningful human oversight.”
• Effect: Restricts fully automated hiring or termination.
5. Notice to Workers and Candidates (1012-1)
• Language: “Any employer that uses an automated employment decision tool…shall notify employees and candidates…(i) that an automated employment decision tool will be used; (ii) the job qualifications…and characteristics…; (iii) what employee data is collected…and the employer’s data retention policy; (iv) the results of the most recent impact assessment…”
• Implications: Increases transparency to end-users and job applicants about how AI is used.
6. Vendor Disclosure Duties (1015-1)
• Language: “Any vendor who…offers for use to an employer an automated employment decision tool shall notify employers that use of such tool is subject to the requirements of this article.”
• Effect: Shifts some compliance burden upstream onto AI vendors.
Section D: Enforcement & Penalties
1. Regulatory Authority (1016-1)
• Language: “The commissioner shall adopt rules and regulations implementing the provisions of this article…and to assess civil penalties as provided in sections two hundred fifteen and two hundred eighteen of this chapter.”
• Potential Penalties: Under §218 (amended), violations of Article 36 can trigger orders to cease use, civil penalties up to double unpaid wages (when analogous wage provisions are violated), or $1,000–$3,000 fines for non-wage violations.
2. Private Right & Attorney General Enforcement (1016-2)
• Language: “The attorney general may initiate…action…including mandating compliance…ordering payment of civil penalties…and recovering damages and liquidated damages.”
3. Rebuttable Presumption Against Retaliation (1014)
• Language: “There shall be a rebuttable presumption of unlawful retaliation if an employer…takes any adverse action against any employee within ninety days of the employee…request for information” or filing a complaint.
• Implications: Protects whistleblowers or employees challenging AI outputs.
Section E: Overall Implications for New York’s AI Ecosystem
1. Restrictive Compliance Costs
• Requiring annual third-party impact assessments, detailed documentation retention, and robust privacy controls will raise operational costs for any organization using AI in hiring or management. This may discourage small firms and startups from adopting these tools.
2. Vendor Burden and Market Effects
• Vendors must inform buyers of all employer obligations under this law (§1015). This disclosure duty could deter non-compliant AI vendors or impose liability risk, leading vendors to drop certain AI functionalities or exit the NY market.
3. Innovation vs. Fairness Trade-off
• By mandating “least discriminatory method” analyses (§1011-1(g)), the bill pushes developers toward bias-mitigation techniques (e.g., fairness-aware ML). While this could spur tool improvement, it also creates legal uncertainties: what exactly satisfies a “least discriminatory” standard may only be clarified via future litigation or regulation.
4. Transparency and Worker Empowerment
• Employee notice and data-access rights (§1012, 1013) increase transparency of AI decisions, potentially driving more ethical AI design. However, employers may restrict or avoid AI features that rely on sensitive data (e.g., biometrics, health) to reduce privacy burdens.
5. Regulatory Oversight and Precedent
• New York would join a small but growing group of jurisdictions (e.g., Illinois, Maryland) that regulate algorithmic hiring. The enforcement model—civil penalty plus AG suits—echoes existing employment-law frameworks, thus leveraging established labor regulators rather than a specialized AI agency.
6. Ambiguities and Future Clarifications
• “Least discriminatory method” (1011-1(g)) is undefined and could be interpreted broadly or narrowly.
• “Meaningful human oversight” (1012-2(b)) is open-ended; regulators or courts will need to clarify exactly what level of human engagement suffices.
In sum, A.3779 directly targets machine-learning and AI-driven employment tools, imposing stringent assessment, transparency, and oversight obligations on both vendors and employers. It is unlikely to promote AI R&D in New York, but rather to constrain the use of AI in labor decisions unless vendors and employers invest heavily in compliance, bias mitigation, and documentation.
Assembly - 3930 - Regulates the use of artificial intelligence in aiding decisions on rental housing and loans
Legislation ID: 59244
Bill URL: View Bill
Sponsors
Assembly - 3991 - Establishes requirements for the use of artificial intelligence, algorithm, or other software tools in utilization review and management
Legislation ID: 59367
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused breakdown of the proposed New York bill (A.3991) organized into the five requested sections. All quotations are taken verbatim from the draft text.
Section A: Definitions & Scope
1. “Artificial intelligence” definition (Insurance Law §107(a)(56))
– Text: “(56) ‘Artificial intelligence’ means an engineered or machine-based system that varies in its level of autonomy and that can, for explicit or implicit objectives, infer from the input it receives how to generate outputs that can influence physical or virtual environments.”
– Relevance: This single, stand-alone paragraph explicitly targets any AI system used by insurers or their vendors. It is broad—it captures both narrow algorithms and more general machine-based “systems,” and it covers any level of autonomy.
2. Scope of application (new §3224-e)
– Text: “A health care service plan or specialized health care service plan that uses an artificial intelligence, algorithm, or other software tool for the purpose of utilization review or utilization management functions … shall comply with this section …” (§3224-e(a))
– Relevance: This clause makes clear that the requirements apply not just to in-house insurer AI, but also to “an entity that uses an artificial intelligence, algorithm, or other software tool” via contract or partnership.
Section B: Development & Research
There are no provisions in the bill that address AI research funding, data-sharing mandates for R&D, or reporting requirements for AI development teams. The entire focus is on operational use of AI in utilization review and management, not on AI creation or early-stage research.
Section C: Deployment & Compliance
The bill imposes eight substantive requirements on any AI, algorithm, or software tool used for utilization review/management:
1. Clinical-data basis (§3224-e(a)(1))
– Text: “The artificial intelligence, algorithm, or other software tool bases its determination on … (i) An enrollee’s medical or dental history; (ii) Individual clinical circumstances …; and (iii) Other relevant clinical information …”
– Impact: Insurers must ensure that AI systems incorporate individual patient records, not generic data or population-level norms.
2. Non-supplantation of provider decision-making (§3224-e(a)(2))
– Text: “The artificial intelligence, algorithm, or other software tool does not supplant health care provider decision making.”
– Impact: AI may only assist, not replace, licensed clinicians; this could restrict fully automated triage or pre-authorization systems.
3. Anti-discrimination (§3224-e(a)(3) & (4))
– Text: “The use … does not adversely discriminate … on the basis of race, color, religion, national origin … or other health conditions.”
– Text: “The artificial intelligence … is fairly and equitably applied.”
– Impact: Insurers must audit for bias in their AI models. Legacy models with proxy variables (e.g., ZIP code) may require re-training or might be prohibited.
4. Transparency (§3224-e(a)(5) & (6))
– Text: “The artificial intelligence … is open to inspection.”
– Text: “Disclosures pertaining to the use and oversight … are contained in the written policies and procedures.”
– Impact: Could require insurers to maintain documentation (model cards, audit logs) and potentially allow state examiners—or even providers/patients—to review AI logic or performance metrics.
5. Ongoing monitoring (§3224-e(a)(7))
– Text: “The artificial intelligence … performance, use, and outcomes are periodically reviewed and revised to maximize accuracy and reliability.”
– Impact: Introduces a continuing-compliance obligation akin to model-risk management in banking, increasing operational costs for vendors.
6. Data-use limitations (§3224-e(a)(8))
– Text: “Patient data is not used beyond its intended and stated purpose, consistent with … HIPAA.”
– Impact: Prevents secondary uses (e.g., marketing) of patient records collected for utilization review; mandates strict data-governance protocols.
7. Safety (§3224-e(a)(9))
– Text: “The artificial intelligence … does not directly or indirectly cause harm to the enrollee.”
– Impact: Leaves undefined what “harm” means in practice, creating possible ambiguity as to liability if an AI denial leads to a medical emergency.
Section D: Enforcement & Penalties
1. Provider sign-off requirement (§3224-e(b))
– Text: “Notwithstanding subsection (a) … a denial, delay, or modification … shall be made by a licensed physician or other health care provider competent to evaluate the specific clinical issues …”
– Mechanism: Any adverse determination must be formally endorsed by a human clinician, placing legal responsibility on that clinician rather than the AI system.
2. Absence of explicit penalties
– Observation: The bill does not specify civil fines, license suspensions, or criminal penalties for non-compliance. Enforcement may fall to the state Insurance Department under its general authority, but no new penalties are enumerated.
Section E: Overall Implications
1. For Insurers and Vendors
– Increased compliance costs to document, audit, and monitor AI tools on an ongoing basis.
– Possible need to restructure contracts and software pipelines to allow for open inspection and human sign-off workflows.
– Re-training or “de-biasing” legacy models to satisfy anti-discrimination and equity clauses.
2. For Providers
– Greater transparency and input in utilization decisions, since AI can’t override clinicians and all adverse determinations must carry a provider’s signature.
– Potential administrative burden in reviewing AI-generated recommendations and in documenting clinical factors.
3. For Patients
– Potentially fairer, more individualized reviews, thanks to requirements that AI incorporate each patient’s history and avoid bias.
– Increased transparency if insurers disclose AI usage policies; but ambiguity around “harm” may leave some recourse uncertain if an AI-based denial has adverse health effects.
4. For Regulators
– A framework to oversee AI in a critical sector (health insurance), but enforcement tools are largely those already in the Insurance Law.
– May require new exam processes, specialized staff to inspect AI systems, and guidelines on what constitutes adequate “open to inspection.”
Ambiguities & Notes
– “Open to inspection” (§3224-e(a)(5)) is undefined: could mean source code review or simply documentation of inputs/outputs.
– “Does not directly or indirectly cause harm” (§3224-e(a)(9)) is vague: regulators may need to clarify whether reputational, financial, or health-outcome harms are covered.
– No explicit timeframes for “periodically reviewed” (§3224-e(a)(7)) or for when disclosures must be made available (§3224-e(a)(6)).
In sum, the bill creates a robust set of guardrails around the deployment of AI in utilization management—mandating transparency, human oversight, and non-discrimination—but leaves enforcement details and certain definitions to future rulemaking or interpretation.
Assembly - 3993 - Prohibits discrimination through the use of clinical algorithms
Legislation ID: 59371
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of Assembly Bill 3993 (2025-2026 session). Since the bill is short and contains only one substantive new section, each part of our analysis necessarily refers to the same text. Whenever possible, I quote the relevant lines and then discuss their AI relevance.
Section A: Definitions & Scope
1. No explicit “AI” or “artificial intelligence” definition appears.
• Quote: “§ 3243-a. Discrimination through use of clinical algorithms.” (lines 1–2)
• Analysis: By referring only to “clinical algorithms,” the bill implicitly encompasses any algorithmic or model-based decision tool used by insurers to make coverage, underwriting, or claims decisions. In modern practice such algorithms are often built with machine learning (ML) or other AI techniques.
2. Protected classes enumerated.
• Quote: “shall not discriminate on the basis of race, color, national origin, sex, age, or disability through the use of clinical algorithms” (lines 3–6)
• Analysis: This anti-discrimination mandate applies to any algorithmic tool that influences insurance outcomes. It does not carve out statistical models, predictive analytics, or ML-based risk scores—they are all “clinical algorithms” under this language.
Section B: Development & Research
There are no clauses in the text that mandate research funding, reporting requirements, or data-sharing rules for AI/ML development. The bill is silent on:
– Publication or audit of algorithmic methodologies.
– Data provenance or sharing among insurers, researchers, or state agencies.
– Collaboration with academic or nonprofit bodies.
Thus, from an R&D perspective, it neither incentivizes nor restricts the development of new AI techniques beyond the anti-discrimination requirement.
Section C: Deployment & Compliance
1. Broad prohibition on discriminatory use.
• Quote: “An insurer subject to this article shall not discriminate … through the use of clinical algorithms in its decision-making.” (lines 3–6)
• Analysis: Any AI-powered risk assessment, underwriting model, or automated claim-adjudication tool must not produce outcomes that disproportionately harm protected groups. Insurers will need to evaluate and possibly redesign or audit existing ML models for bias.
2. Exception for disparity-reduction.
• Quote: “This section shall not prohibit the use of clinical algorithms that rely on variables to appropriately make decisions, including to identify, evaluate, and address health disparities.” (lines 7–9)
• Analysis: Algorithms explicitly designed to detect or remediate inequities—e.g., tools that surface under-served populations or flag differential outcomes—are permitted. This carve-out encourages development of AI tools for monitoring and reducing bias.
Section D: Enforcement & Penalties
The bill does not specify new enforcement mechanisms, penalties, or remedies. Enforcement presumably defaults to existing Insurance Law provisions (Article 32 enforcement by the Superintendent of Financial Services), but no text here spells out:
– Civil fines or administrative penalties for breach of § 3243-a.
– Private right of action for individuals harmed by discriminatory algorithms.
– Required corrective plans or public reporting.
This omission leaves ambiguity: regulators must interpret how to enforce the prohibition without additional statutory guidance.
Section E: Overall Implications for New York’s AI Ecosystem
1. Restriction Effect: Insurers using AI/ML models will face a compliance burden to demonstrate that their models do not produce discriminatory outcomes. They may need new bias-detection tools, external audits, or revised data pipelines.
2. Innovation Incentive: The carve-out for disparity-addressing algorithms encourages development of fairness-enhancing AI tools such as adversarial debiasing, equality-of-opportunity auditing, and explainable AI that can identify and mitigate group-level disparities.
3. Regulatory Ambiguity: Lack of explicit enforcement provisions and absence of defined standards (e.g., thresholds for disparity, testing protocols) means insurers and vendors lack clear rules of the road. They may seek rule-making or guidance from the state regulator, increasing uncertainty and potential litigation risk.
4. Impact on Stakeholders:
– Researchers and startups focused on fairness in healthcare AI may find new demand among insurers needing compliance solutions.
– Established vendors will likely update or deprecate legacy risk models that cannot meet non-discrimination requirements.
– End-users (patients, policyholders) may benefit from more equitable access to coverage and treatment recommendations.
– Regulators will need to develop technical expertise to evaluate insurer compliance in the context of opaque AI systems.
In sum, although the bill does not engage deeply with AI governance (no definitions, enforcement details, or procedural rules), by outlawing discriminatory “clinical algorithms” it implicitly covers any AI/ML-based decision tools in health insurance. It pressures the market toward bias-audited, equity-oriented algorithmic products while leaving significant room for interpretive rule-making.
Assembly - 433 - Relates to the disclosure of automated employment decision-making tools and maintaining an artificial intelligence inventory
Legislation ID: 54210
Bill URL: View Bill
Sponsors
Assembly - 4427 - Relates to the use of external consumer data and information sources being used when determining insurance rates
Legislation ID: 60078
Bill URL: View Bill
Sponsors
Assembly - 4550 - Requires the department of labor to study the long-term impact of artificial intelligence on the state workforce
Legislation ID: 60326
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a section-by-section analysis of Assembly Bill 4550 (2025-2026), focusing on its AI-related provisions. All citations refer to line numbers in the bill as introduced February 4, 2025.
Section A: Definitions & Scope
1. No explicit definition of “artificial intelligence.”
– The bill repeatedly uses the term “artificial intelligence” (AI) but does not define it.
• “long-term impact of artificial intelligence on the state workforce” (Lines 4–5).
• “prohibited from using artificial intelligence in any way…” (Line 18).
– Implication: Agencies may interpret “AI” variably—e.g., machine learning models, expert systems, automation scripts—creating uncertainty in compliance.
2. Scope of Entities Covered
– “every state department, board, bureau, division, commission, committee, public authority, public corporation, council, office or other governmental entity performing a governmental or proprietary function for the state” (Lines 15–22).
– This is an extremely broad sweep, covering nearly every part of state government.
Section B: Development & Research
1. Mandated Study and Reporting Requirements
– “No later than six months after the effective date… the department of labor, in consultation with the department of civil service and the office of information technology services, shall begin a study on the long-term impact of artificial intelligence on the state workforce including but not limited to job performance, productivity, training, education requirements, privacy and security.” (Lines 1–7)
• Advances knowledge by requiring inter-agency collaboration on AI’s workforce effects.
• Could spur data collection, hiring of AI analysts, and engagement with academic partners.
– Reporting cadence:
• Interim reports every five years (Lines 7–9).
• Final report by January 1, 2035 (Lines 9–11).
• Recipients: governor, legislative leadership (Lines 10–13).
– Potential impact on researchers: Provides a steady demand for studies, surveys, economic modeling of AI’s workforce impact. May unlock state funding or data-sharing agreements.
Section C: Deployment & Compliance
1. Moratorium on AI-driven Displacement of State Workers
– “Until the final report and recommendations are received pursuant to subdivision 1… every… state… entity… shall be prohibited from using artificial intelligence in any way that would displace any natural person from their employment…” (Lines 14–22).
• Restricts procurement or deployment of any AI system with an automation component that could eliminate a current job.
• Applies equally to contract-of-services vendors and in-house systems.
2. Ambiguity in “Displace”
– The term “displace” is not defined. Potential interpretations:
• Any elimination of a position?
• Substantial reduction in hours or grade?
• Full automation of a job function?
– Agencies may struggle to distinguish permissible augmentation (e.g., AI-assisted reporting tools) from prohibited displacement.
Section D: Enforcement & Penalties
1. No Express Civil or Criminal Penalties
– The bill imposes “prohibition[s]” but does not specify fines, injunctions, or other enforcement mechanisms for non-compliance.
– Enforcement may default to internal executive-branch oversight or later implementing regulations.
2. Legislative Leverage via Budget
– Although not stated, the legislature could condition agency budgets on compliance with the moratorium.
Section E: Overall Implications
1. Research & Data Gathering Boost
– By mandating a multi-agency study with interim and final reports (Lines 1–13), the bill elevates AI’s workforce impact to a sustained policy priority.
– Could lead to establishment of an AI-workforce data repository, regular stakeholder convenings, and pilot programs for AI upskilling.
2. Deployment Freeze May Stifle Innovation
– The blanket moratorium (Lines 14–22) effectively bars any state-level AI adoption that risks headcount reduction until 2035.
– Likely discourages both in-house experimentation and private-sector vendors from proposing automated solutions to state agencies.
– May slow efficiency gains, cost savings, and improved citizen services that AI tools can deliver.
3. Uncertainty and Administrative Burden
– Lack of definitions creates compliance risk. Agencies may avoid any AI use—even benign cases—rather than risk non-compliance.
– Consulting requirements and report drafting will draw staff time away from operational duties.
4. Path Forward for Legislators and Regulators
– Clarify “artificial intelligence” and “displace” to narrow the moratorium to high-risk use cases (e.g., hiring, performance evaluation).
– Specify enforcement mechanisms and carve out “augmentation” uses.
– Consider a sunset or periodic legislative review of the moratorium in light of interim reports.
By combining a long-range study mandate with a near-total freeze on AI-driven job displacement, A.4550 seeks both to build an evidentiary foundation for future legislation and to safeguard current state employees. However, without clearer definitions or enforcement protocols, the bill may create legal uncertainty and hamper beneficial AI deployments across New York State government.
Assembly - 5216 - Requires state units to purchase a product or service that is or contains an algorithmic decision system that adheres to responsible artificial intelligence standards
Legislation ID: 61793
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a provision‐by‐provision analysis of A. 5216. All quotations refer to the introduced text of the bill.
Section A. Definitions & Scope
1. “Algorithmic decision system”
• Text: “(i) ‘algorithmic decision system’ means a computational process, including one derived from machine learning, statistics, or other data processing or artificial intelligence techniques, that makes a decision, or facilitates human decision making, in a manner that impacts individuals;” (State Finance Law §165(10)(a)(i), lines 5–9)
• Relevance: This is the bill’s core AI definition. It explicitly covers machine learning, statistical, or AI‐derived systems used for decision making.
2. “State unit”
• Text: “(ii) ‘state unit’ means the state and any governmental agency or political subdivision or public benefit corporation of the state.” (Id. §165(10)(a)(ii), lines 10–12)
• Relevance: Establishes the entities—agencies, subdivisions, public authorities—whose procurements of AI systems will be governed by the new rules.
3. Expansion of “unlawful discriminatory practice” to ADS
• Text: “includes an act prohibited under paragraph (a) of this subdivision that is performed through an algorithmic decision system, as defined under subdivision ten of section one hundred sixty-five of the state finance law.” (Executive Law §292(4)(b), lines 19–22)
• Relevance: This tie‐in means that discrimination committed via any ADS now falls under New York’s human rights law.
Section B. Development & Research
— No clauses in this bill directly mandate AI research funding, data‐sharing, or reporting for developers or researchers. Its focus is procurement and anti‐discrimination.
Section C. Deployment & Compliance (Procurement of AI by State Units)
1. Responsible AI standards requirement
• Text: “When purchasing a product or service that is or contains an algorithmic decision system … a state unit shall purchase a product or service that adheres to responsible artificial intelligence standards, including:” (State Finance Law §165(10)(b), lines 13–15)
• Relevance: All state purchases of AI‐driven products or services must now meet “responsible AI” criteria.
2. Harm avoidance
• Text: Requires “the avoidance of harm, including the minimization of: (A) risks of physical or mental injury; (B) the unjustified deletion or disclosure of information; and (C) the unwarranted damage to property, reputation, or environment;” (Id. §165(10)(b)(i), lines 16–20)
• Impact: Vendors must demonstrate risk assessments and mitigation plans around safety, privacy, and reputation.
3. Transparency
• Text: Requires “full disclosure to the state unit [of] any algorithmic decision system: (A) capabilities; (B) limitations; and (C) potential problems;” (Id. §165(10)(b)(ii), lines 20–23)
• Impact: Puts documentation burdens on suppliers—e.g., model cards, risk disclosures, known failure modes.
4. Fairness
• Text: “Giving primacy to fairness, including by taking actions to: (A) eliminate discrimination; (B) include equality, tolerance, respect for others, and justice as algorithmic decision system goals; and (C) provide an avenue for feedback to redress harms;” (Id. §165(10)(b)(iii), lines 3–8)
• Impact: State units will demand demonstration of bias testing, impact assessments, and remediation processes.
5. Risk evaluation
• Text: “A comprehensive and thorough evaluation and analysis of the algorithmic decision system’s impact and potential risks.” (Id. §165(10)(b)(iv), lines 8–9)
• Impact: Likely calls for third‐party audits or internal risk analysis reports prior to purchase.
6. Rulemaking authority
• Text: “The commissioner of taxation and finance shall adopt regulations to carry out this subdivision.” (Id. §165(10)(c), lines 10–11)
• Impact: The details (e.g., templates, thresholds) will be shaped in forthcoming regulations.
Section D. Enforcement & Penalties
1. Inclusion in Human Rights Law
• Text: “The term ‘unlawful discriminatory practice’ … includes an act prohibited … that is performed through an algorithmic decision system…” (Executive Law §292(4)(b), lines 19–22)
• Effect: Any discriminatory outcome produced by an AI system can trigger an administrative complaint, investigation by the Division of Human Rights, and associated remedies (injunctions, civil penalties, damages).
2. Procurement compliance
• While the bill does not specify civil or criminal penalties for non‐compliant procurements, the regulation‐making process under §165(10)(c) could impose contract sanctions, debarment, or withholding of payment if vendors fail to meet responsible AI standards.
Section E. Overall Implications
1. Advance responsible AI adoption in state government
• By conditioning all state purchases on adherence to “responsible AI standards,” the bill pushes vendors to operationalize harm mitigation, transparency, and fairness at scale. This could raise the bar for responsible AI practices across the market.
2. Compliance burden for vendors and startups
• Smaller AI vendors may confront significant new costs—producing impact assessments, disclosures, and audit documentation—to qualify for state contracts. It could favor larger, well-capitalized incumbents.
3. Regulatory clarity still pending
• The substantive requirements are broad (“comprehensive and thorough evaluation,” “fairness,” “avoidance of harm”) but lack implementation detail. The forthcoming regulations will determine whether requirements are prescriptive (e.g., mandatory bias tests) or principle-based.
4. Stronger enforcement against AI-enabled discrimination
• Amending the Human Rights Law to cover “acts … performed through an algorithmic decision system” makes clear that algorithmic bias is actionable under existing discrimination statutes, providing a novel legal lever for impacted individuals.
5. Ambiguities and possible interpretations
• “Potential problems” (§165(10)(b)(ii)(C)) could be interpreted to require vendors to report all known model weaknesses or only harms with material legal risk.
• “Comprehensive and thorough evaluation” (§165(10)(b)(iv)) is undefined in scope—could mandate external third-party audits or allow self-certification under guidelines.
In sum, A. 5216 intervenes early in the AI lifecycle—at procurement—and overlays state purchases with a principles-based responsible AI framework, while also equating AI‐driven discrimination with unlawful practices under New York’s Human Rights Law. The bill’s ultimate impact will hinge on the specificity of the implementing regulations.
Assembly - 5429 - Establishes the New York workforce stabilization act requiring certain businesses to conduct artificial intelligence impact assessments on the application and use of such artificial intelligence
Legislation ID: 62191
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a structured analysis of A. 5429 (“New York workforce stabilization act”) with each claim tied to quoted language.
Section A: Definitions & Scope
1. “Artificial intelligence” (AI) – The bill does not offer a standalone definition, but repeatedly uses the term to encompass systems using “algorithms, computational models, artificial intelligence techniques, robotic hardware, or a combination thereof.”
• Citation: Tax‐law surcharge on displacement—“system or process that uses algorithms, computational models, artificial intelligence techniques, robotic hardware, or a combination thereof, to automate, support, or replace human labor” (§ 186-h 1(a), lines 23–28).
2. “Data mining” – Explicitly defined for the surcharge on AI‐based data analysis.
• Citation: “For the purposes of this subdivision, the term ‘data mining’ shall mean a process involving pattern-based queries, searches, or other analyses of one or more electronic databases.” (§ 186-h 2(a), lines 4–8).
3. “Employer” vs. “Small business” – Scope of who must comply with the impact‐assessment rules.
• “Employer” = resident business, not small, > 100 employees (§ 201-j 3(a), lines 14–18).
• “Small business” = resident, ≤ 100 employees, independent, non-dominant (§ 201-j 3(b), lines 17–20).
Section B: Development & Research
Although the bill does not directly fund R&D, the impact‐assessment requirement forces employers developing or adopting AI to disclose development details.
1. Algorithmic transparency: employers must “summarize underlying algorithms, computational modes, and tools.”
• Citation: § 201-j 1(c)(i), lines 19–22: “a summary of the underlying algorithms, computational modes, and tools that are used within the artificial intelligence.”
2. Training‐data disclosure: mandates description of “design and training data used to develop the artificial intelligence process.”
• Citation: § 201-j 1(c)(ii), lines 21–22.
3. Evaluation of objectives: requires “an evaluation of the ability of the artificial intelligence to achieve its stated objectives.”
• Citation: § 201-j 1(b), lines 15–17.
Potential impact on researchers/startups:
– Pros: Clarifies transparency expectations; may build public trust.
– Cons: Additional compliance burden and possible IP concerns around revealing algorithms/data.
Section C: Deployment & Compliance
This section examines how the bill regulates AI deployment in the workplace.
1. Regular impact assessments every two years, plus pre‐implementation for any “material change.”
• Citation: § 201-j 1, lines 9–12: “at least once every two years” and “prior to any material change to the artificial intelligence that may change the outcome or effect.”
2. Assessment content: beyond algorithmic summary, must cover data sensitivity, storage, user controls.
• Citation: § 201-j 1(d), lines 1–4: “the extent to which the deployment and use of the artificial intelligence requires input of sensitive and personal data… how that data is used and stored, and any control users may have over their data.”
3. Submission deadline: must submit to Department of Labor ≥ 30 days before implementation.
• Citation: § 201-j 2, lines 10–12.
For established vendors, compliance protocols and auditing functions will need to be built into product offerings or consultancy services. Regulators will gain a formal review window (30 days) to vet new AI uses.
Section D: Enforcement & Penalties
1. Civil surcharge on corporations that displace ≥ 15 employees via AI or data mining—2 percent of the business income base.
• Citation: § 186-h 1(a), lines 23–29: “surcharge … at the rate of two percent of the corporation’s business income base.”
• Citation: § 186-h 2(a), lines 4–6: identical 2 percent rate for data-mining users.
2. Reporting and payment annually with commissioner’s return form.
• Citation: §§ 186-h 1(b), 2(b).
3. Waivers: Commissioner (with Dept. of Labor) may waive the displacement surcharge for:
• Businesses facing genuine labor shortages;
• Agricultural producers needing automation for competitiveness;
• Small businesses that “require the use of … AI … to remain economically viable.”
• Citation: § 186-h 1(c)(i)–(iii), lines 36–49.
4. Deposit and use of surcharge revenues: funds routed to workforce retraining, development programs, or unemployment insurance.
• Citation: § 186-h 4, lines 43–53.
There are no criminal penalties spelled out; failure to pay the surcharge would presumably follow existing tax‐collection remedies under Art. 28.
Section E: Overall Implications
• Transparency & Accountability: The mandated AI impact assessments (IAAs) will force large employers to publicly document objectives, algorithms, data usage, and displacement estimates. Regulators gain early insight (30-day review) into new AI deployments, potentially slowing rapid, untested rollouts.
• Innovation vs. Compliance Cost: Startups/SMBs below 100 employees are exempt from IAAs but, if they grow, will face steep disclosure requirements. Displacement and data‐mining surcharges may deter aggressive automation strategies or drive companies to externalize AI functions to third parties.
• Worker Protections & Retraining: Revenue from the surcharge is earmarked for retraining and workforce development, signaling a shift to cushion workers displaced by AI.
• Ambiguities & Risks:
– “Material change” to AI systems is undefined and subject to interpretation—could lead to disputed compliance triggers (§ 201-j 1).
– The boundary between “data mining” and routine analytics may be contested when determining surcharge applicability.
In sum, A. 5429 blends regulatory oversight (via IAAs) with economic disincentives (surcharges) to shape a more measured and worker-centric AI adoption in New York.
Assembly - 606 - Relates to requiring advertisements to disclose the use of a synthetic performer
Legislation ID: 54383
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an analysis of A.606 (2025–2026 session), which amends New York’s General Business Law to require disclosure of synthetic performers in advertisements. Each section of this response cites verbatim text from the bill.
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Section A: Definitions & Scope
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1. “Generative artificial intelligence” (GAI)
– Text: “(a) For the purposes of this section, ‘generative artificial intelligence’ means the use of machine learning technology, software, automation, and algorithms to perform tasks, to make rules and/or predictions based on existing data sets and instructions, including, but not limited to: (i) Any artificial system that performs tasks under varying and unpredictable circumstances without significant human oversight … (ii) An artificial system developed … that solves tasks requiring human-like perception … (iii) An artificial system designed to think or act like a human … (iv) A set of techniques, including machine learning, that is designed to approximate a cognitive task; and/or (v) An artificial system designed to act rationally…” (Bill, § 1, lines 3–22)
– Analysis: This broadly captures most modern AI models—neural networks, large-language models, computer-vision systems, planning agents, etc. Its open-ended “including, but not limited to” language could sweep in future AI approaches.
2. “Synthetic performer”
– Text: “(b) For purposes of this section, ‘synthetic performer’ means a digitally created asset created, reproduced, or modified by computer, using generative artificial intelligence or a software algorithm, that is intended to create the impression that the asset is a natural performer who is not recognizable as any identifiable natural performer.” (Bill, § 1, lines 23–28)
– Analysis: Targets AI-generated images, videos, voice, avatars, or composite performers. “Not recognizable as any identifiable natural performer” implies deepfakes or wholly-synthetic actors, not licensed human talent.
3. Scope of “advertisements”
– Text: “Any person … engaged in the business of dealing in any property … who … makes … any advertisement respecting any such property … in any medium …” (Bill, § 1, lines 41–45)
– Analysis: Applies to all commercial ads (print, broadcast, online). No carve-out for small publishers or user-generated content platforms.
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Section B: Development & Research
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There are no provisions in this bill that directly address AI research funding, data-sharing mandates, academic-industry collaboration, or reporting requirements. The focus is strictly on disclosure in consumer-facing advertisements.
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Section C: Deployment & Compliance
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1. Mandatory disclosure
– Text: “Any person … who for any commercial purpose makes … any advertisement … shall disclose in such advertisement if a synthetic performer is in such advertisement, where such person has actual knowledge.” (Bill, § 1, lines 45–48)
– Analysis: Advertisers must include an explicit statement—e.g. “Featuring synthetic performer created with AI.” The requirement hinges on “actual knowledge,” which may leave open questions about willful blindness or negligence.
2. Ambiguity: “Actual knowledge”
– Possible interpretations:
• Narrow: Only if the advertiser expressly knows the asset is AI-generated.
• Broad: Could include cases where a reasonable person should know (e.g. purchased from an AI-asset marketplace).
3. Breadth of media covered
– Text: “in any medium or media in which such advertisement appears” (Bill, § 1, line 44)
– Analysis: Includes digital channels (websites, social media, streaming), although these are not enumerated. Platforms may need compliance policies and labeling tools.
––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
Section D: Enforcement & Penalties
––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
1. Civil penalties
– Text: “A violation of this subdivision shall result in a civil penalty of one thousand dollars for a first violation, and five thousand dollars for any subsequent violation.” (Bill, § 1, lines 48–50)
– Analysis: Relatively modest fines aimed at small- to medium-sized advertisers; repeat offenders face steeper penalties.
2. Relationship to existing law
– Text: “Nothing in this section shall limit or reduce any rights any person may have under section fifty … of the civil rights law …” (Bill, § 1, lines 50–53)
– Analysis: Does not preempt anti-discrimination or privacy claims; those remain separately enforceable.
3. Safe harbor for platforms
– Text: “Nothing in this section shall be construed to limit, or to enlarge, the protections that 47 U.S.C. section 230 confers on an interactive computer service for content provided by another information content provider” (Bill, § 1, lines 53–56)
– Analysis: Online publishers and social media platforms retain their federal immunity from liability for user-posted AI-generated ads.
––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
Section E: Overall Implications
––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
• Transparency & Consumer Trust
– By requiring clear labeling of AI performers, the state aims to prevent deception (e.g. deepfake endorsements or AI-generated models in product ads).
• Burden on Advertisers & Platforms
– All advertisers must implement compliance checks; platforms will likely need to offer labeling mechanisms. Startups and small businesses may face setup costs.
• Limited Scope
– The bill does not regulate AI development, data practices, or system safety—it solely addresses post-production marketing materials.
• Enforcement & Chilling Effects
– Civil fines are modest but could multiply with multiple ads. Some advertisers may avoid AI-generated creative altogether to sidestep compliance risk.
––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
By targeting “synthetic performers” created via “generative artificial intelligence,” A.606 creates a narrow but concrete requirement for transparency in AI-generated advertising content. It leaves broader AI research, deployment, and accountability issues to future legislation.
Assembly - 6180 - Excludes a production using artificial intelligence or autonomous vehicles in a manner which results in the displacement of employees from the definition of qualified film
Legislation ID: 63687
Bill URL: View Bill
Sponsors
Assembly - 6453 - Relates to the training and use of artificial intelligence frontier models
Legislation ID: 64230
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. “Artificial intelligence” and “Artificial intelligence model”
– The bill defines “Artificial intelligence” as “a machine-based system that … uses machine- and human-based inputs to perceive real and virtual environments, abstract such perceptions into models … and use model inference to formulate options for information or action.” (§ 1420.2, lines 22–25).
– It defines “Artificial intelligence model” as “an information system … that implements artificial intelligence technology and uses computational, statistical, or machine-learning techniques to produce outputs from a given set of inputs.” (§ 1420.3, lines 4–7).
– Relevance: These broad definitions capture virtually all modern AI/ML systems, from neural nets to recommendation engines.
2. “Frontier model”
– Defined as any AI model trained with over 10^26 operations or a compute cost over USD 100 million, and smaller models “produced by applying knowledge distillation” to such models. (§ 1420.6, lines 18–24).
– Relevance: Targets the most computationally intensive, state-of-the-art AI systems. Any entity training or using these “frontier” models falls under the act’s strictures.
3. “Large developer”
– Means any person who has trained at least one frontier model (compute cost > USD 5 million) and spent > USD 100 million aggregate on training frontier models, excluding academic research. (§ 1420.9, lines 46–54).
– Relevance: Establishes thresholds above which obligations (reporting, safety protocols, audits) apply. Smaller AI practitioners are largely exempt until they cross these financial/compute thresholds.
4. “Safety and security protocol”
– Means “documented technical and organizational protocols” describing administrative/technical controls, testing procedures, compliance steps, and designation of senior personnel. (§ 1420.12, lines 10–33).
– Relevance: Imposes formal governance processes around AI system development and deployment.
5. “Safety incident” and “critical harm”
– “Safety incident” includes unauthorized model release, autonomous harmful behavior, or failure of safeguards. (§ 1420.13, lines 34–43).
– “Critical harm” is defined as death/serious injury to ≥ 100 people or ≥ USD 1 billion damages, via weapons creation or AI-enabled criminal behavior. (§ 1420.7, lines 25–38).
– Relevance: Sets high thresholds for what constitutes reportable risk events.
Section B: Development & Research
1. Pre-training obligations for prospective large developers
– Anyone on track to cross USD 5 million (single model) or USD 100 million (aggregate) compute spend must “implement a written safety and security protocol” and “conspicuously publish a copy … with appropriate redactions … to the attorney general.” (§ 1421.9(a–b), lines 28–41).
– Impact: Forces early-stage high-compute projects to adopt governance before completing model training, potentially increasing startup costs and administrative overhead.
2. Academic research carve-out
– Accredited colleges and universities “shall not be considered large developers … to the extent … engaging in academic research.” (§ 1420.9, lines 49–54).
– Impact: Encourages open research in universities without regulatory burdens, but may disincentivize partnerships if private compute funding drives costs above thresholds.
Section C: Deployment & Compliance
1. Safety protocol publication and retention
– Before deploying any frontier model, a large developer must:
• Implement and retain an unredacted safety/security protocol for the model’s lifespan + 5 years (§ 1421.1(a–b), lines 4–9).
• “Conspicuously publish” a redacted protocol and send it to the attorney general, granting AG access to the unredacted version on request (§ 1421.1(c), lines 10–14).
• Record and retain test procedures/results sufficient to “replicate the testing procedure” for lifespan + 5 years (§ 1421.1(d), lines 15–19).
• “Implement appropriate safeguards to prevent unreasonable risk of critical harm.” (§ 1421.1(e), line 21).
– Impact: Creates high transparency and record-keeping burdens; may deter deployment of cutting-edge models or push developers offshore.
2. No deployment under unacceptable risk
– “A large developer shall not deploy a frontier model if doing so would create an unreasonable risk of critical harm.” (§ 1421.2, lines 22–24).
– Ambiguity: “Unreasonable risk” is undefined; open to interpretation by AG or courts.
3. Annual review and updates
– Mandatory annual review of safety protocols to reflect “changes to capabilities” and industry best practices; must republish if modified (§ 1421.3, lines 24–30).
– Impact: Ensures protocols evolve with AI capabilities, but adds ongoing compliance costs.
4. Third-party audits
– Annual independent compliance audit by a third party chosen by the developer; report must cover compliance steps, non-compliance findings, internal controls, and auditor signature (§ 1421.4(a–c), lines 32–52).
– Reports retained unredacted for lifespan + 5 years, redacted versions published, unredacted available to AG (§ 1421.4(d–e), lines 53–58).
– Impact: Significant audit costs; may create market for specialized AI safety auditors.
5. Compute-cost reporting
– Developers must submit “updated total compute cost” for frontier models alongside audit reports (§ 1421.5, lines 6–8).
– Impact: AG can track who qualifies as a large developer; but confidential cost data may be commercially sensitive.
6. Safety incident reporting
– Any “safety incident” must be reported to AG within 72 hours of learning of it, with date, qualifying reasons, and plain-language description (§ 1421.6, lines 9–17).
– Impact: Rapid incident reporting akin to data breach laws; startups may struggle with legal definitions of incidents.
7. Limited federal contract carve-out
– Section doesn’t apply where it “strictly conflicts” with federal government contract terms. (§ 1421.7(a), lines 18–20).
– But applies to any non-federal uses, even if model was developed under federal contract (§ 1421.7(b), lines 21–24).
– Impact: Federal contractors must navigate dual compliance regimes.
Section D: Enforcement & Penalties
1. Civil actions by attorney general
– Violations of transparency/deployment rules (§ 1421) carry penalties up to 5 percent of total compute cost for a first violation, 15 percent for subsequent (§ 1423.1(a), lines 20–26).
– Violations of employee protections (§ 1422) carry up to USD 10 000 per employee (§ 1423.1(b), lines 27–30).
– AG may also seek injunctive or declaratory relief (§ 1423.1(c), lines 31–33).
2. Anti-retaliation and whistleblower rights
– Section 1422 bars retaliation against employees who report “unreasonable or substantial risk of critical harm” to the developer or AG (§ 1422.1, lines 43–49).
– Employees may seek injunctive relief (§ 1422.2, lines 50–52). Failure to post notice of rights is prohibited (§ 1422.3, lines 52–55).
3. Void contract terms and joint liability
– Any contractual waiver of liability for violations is “void as a matter of public policy” (§ 1423.2(a), lines 33–38).
– Courts may pierce corporate veils if entities structured to evade liability (§ 1423.2(b), lines 43–49).
Section E: Overall Implications
1. Heightened compliance burden for major AI actors
– Large developers face extensive documentation, auditing, and reporting requirements. This could slow deployment and raise costs, favoring incumbents with compliance resources.
2. Clearer AI governance pathways
– The act provides a structured approach to AI safety via “safety and security protocols” and incident reporting, potentially improving public trust.
3. Innovation vs. regulation balance
– Academic carve-outs and federal contract exceptions mitigate restrictions for research and national defense. However, undefined terms like “unreasonable risk” create legal uncertainty.
4. Enforcement teeth
– Compute-cost–tied penalties and voidance of liability waivers give the AG strong leverage. Companies may incorporate preventive controls or relocate activities.
5. Ecosystem effects
– Startups nearing the “large developer” thresholds may reconsider scaling. A new market for AI safety auditors and compliance advisors is likely to emerge. Regulators must clarify ambiguous standards to avoid chilling innovation.
Assembly - 6540 - Requires generative artificial intelligence providers to include provenance data on certain content made available by the provider
Legislation ID: 64440
Bill URL: View Bill
Sponsors
Assembly - 6545 - Imposes liability for damages caused by a chatbot impersonating licensed professionals
Legislation ID: 64450
Bill URL: View Bill
Sponsors
Assembly - 6578 - Establishes the artificial intelligence training data transparency act
Legislation ID: 64494
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a section-by-section analysis of Assembly Bill A6578 (“Artificial Intelligence Training Data Transparency Act”), with verbatim citations and commentary on relevance, impact, and ambiguities.
Section A: Definitions & Scope
1. “Artificial intelligence” / “Artificial intelligence technology”
– Citation: “§ 1421. 1. ‘Artificial intelligence’ or ‘artificial intelligence technology’ means a machine-based system that …” (lines 15–21).
– Analysis: This definition explicitly targets any system “that uses machine- and human-based inputs to perceive real and virtual environments… and use model inference to formulate options.” By choosing a broad, functional description rather than naming particular techniques, the bill covers rule-based systems, classical machine learning, and modern deep-learning approaches alike.
2. “Generative artificial intelligence”
– Citation: “§ 1421. 3. ‘Generative artificial intelligence’ means a class of AI models that are self-supervised and emulate the structure and characteristics of input data to generate derived synthetic content…” (lines 3–6).
– Analysis: By singling out generative models—including those that output text, images, audio, etc.—the bill zeroes in on the fastest-growing subfield (e.g. large language models, diffusion image models). It implicitly excludes discriminative classifiers, focusing only on systems that produce new content.
3. “Developer” and “Substantially modifies”
– Citation: “§ 1421. 2. ‘Developer’ means … a corporation that designs, codes, produces, or substantially modifies an artificial intelligence model or service for use by members of the public.” (lines 22–2). “§ 1421. 4. ‘Substantially modifies’ or ‘substantial modification’ means a new version, new release, or other update … that materially changes its functionality or performance…” (lines 7–10).
– Analysis: The term “developer” is broadly defined to sweep in private firms, non-profits, and even government entities. “Substantial modification” covers major updates (e.g. fine-tuning). A developer must re-report when it “substantially modifies” a system, ensuring ongoing transparency.
4. Other definitions
– “Synthetic data generation” (§ 1421. 5, lines 11–13)
– “Train a generative… model or service” (§ 1421. 6, lines 14–16)
– “Aggregate consumer information,” “AI model,” etc.
– Analysis: These further refine the bill’s scope. Notably “synthetic data generation” is called out (§ 1421.5), so developers using simulated inputs must disclose that fact later in § 1422.
Section B: Development & Research
No direct R&D funding or mandates appear. However, the bill imposes a reporting requirement on training data.
1. Training-data documentation requirement
– Citation: “§ 1422. 1. On or before January first, two thousand twenty-six … the developer … shall post on the developer’s website documentation regarding the data used by the developer to train the generative artificial intelligence model or service, including a high-level summary of the datasets…” (lines 28–36).
– Impact: Researchers and startups must inventory and publicly summarize their entire training corpus. This likely raises operational costs (data audits, legal reviews) and may deter small entities lacking resources to prepare these disclosures. Established vendors may absorb costs more easily but still face brand-risk if their data sources include questionable copyrighted content.
2. Required disclosure elements
– Citations:
• “(a) the sources or owners of the datasets;” (line 40)
• “(b) a description of how the datasets further the intended purpose…” (line 41–42)
• “(c) the number of data points …” (lines 43–44)
• … through “(l) whether the … service used or continuously uses synthetic data generation…” (lines 13–17).
– Impact: These granular data points—especially (a), (e) copyright status, and (g) personal information—help end-users and regulators assess legal and privacy risks. For research labs, disclosing data-point counts (c) could reveal proprietary scale advantages.
3. Exemptions
– Citation: “§ 1422. 2. A developer shall not be required … if … sole purpose is operation of aircraft in the national airspace; or … developed for national security … only to a federal entity.” (lines 21–25).
– Impact: Standard carve-outs for FAA-regulated systems and defense contractors. Absent broader R&D exceptions, nearly all academic labs publishing public models will fall under the transparency rules.
Section C: Deployment & Compliance
1. Continuous compliance trigger
– Citation: “… prior to each time thereafter that a generative AI model or service, or a substantial modification … is made publicly available to New Yorkers for use … the developer … shall post … documentation…” (lines 28–35).
– Analysis: Compliance is tied to release events, meaning every major “release,” “update,” or even “fine-tuning” (per § 1421.4) mandates a fresh disclosure. Frequent-releasing providers (e.g., weekly model updates) face an ongoing administrative burden.
2. Employee data transparency
– Citation: “§ 1423. 1. Any … entity that … substantially modifies a generative AI model … using data … derived from individuals employed or contracted by the entity … shall ensure that the following information is disclosed to each employee …” (lines 27–34).
– Required employee notices: intended purpose (a), dataset description (b–c), personal info status (d), usage dates (e–f).
– Impact: For internal R&D, employers must notify staff when their code, communications, or work-product feed models. This empowers employee privacy rights but adds HR/legal overhead for startups and universities.
Section D: Enforcement & Penalties
The bill text lacks any explicit enforcement mechanism, civil penalties, or private right of action.
– Ambiguity: Without specified fines or enforcement agency authority, compliance depends on developers’ good faith. Regulators or AG enforcement powers under GBL generally could apply, but the absence of clear penalties creates uncertainty.
Section E: Overall Implications
1. Transparency vs. Innovation
– By mandating comprehensive public disclosures of training data, the bill aims to illuminate bias sources, intellectual-property claims, and privacy exposures. However, the administrative load disproportionately burdens smaller developers and academic teams, potentially chilling open-source research.
2. Competitive dynamics
– Large incumbents likely have the compliance infrastructure to manage these disclosures (legal, engineering, data governance teams). Startups and labs may face elevated fixed costs that raise the bar to entry.
3. Regulatory precedent
– New York would become one of the first U.S. jurisdictions to require public training-data transparency for generative AI, presaging federal or other state efforts. The lack of enforcement provisions may reduce immediate legal risk but invites future amendments to add penalties.
4. Ambiguities
– “High-level summary” is undefined—could range from a few bullet points to complete dataset inventories.
– “Publicly available to New Yorkers” raises questions: must the developer geofence disclosures or the model itself?
In sum, A6578 explicitly targets the training and fine-tuning stages of generative AI (Sections 1422–1423) by requiring data-source transparency both to end users and to employees. Its broad definitions sweep in nearly all AI modalities, while its ongoing compliance triggers create a recurring reporting obligation. The measure prioritizes consumer and worker information rights over innovation incentives, and its success will hinge on clarifying enforcement, summary standards, and scope of “public availability.”
Assembly - 6656 - Relates to requiring responsible capability scaling policies
Legislation ID: 64602
Bill URL: View Bill
Sponsors
Assembly - 6765 - Enacts the preventing algorithmic pricing discrimination act
Legislation ID: 98289
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of A.6765-A (“Preventing Algorithmic Pricing Discrimination Act”), organized as you requested. All claims are tied to exact bill language.
Section A: Definitions & Scope
1. “Algorithm” (§349-a.1(a), lines 7–9)
– Text: “ ‘Algorithm’ means a computational process that uses a set of rules to define a sequence of operations.”
– Relevance to AI: This is the foundational definition for anything we’d call an “AI system” or “machine-learning model,” since most AI (especially older expert systems and modern ML pipelines) follows rule-based or learned computational processes.
2. “Dynamic pricing” (§349-a.1(e), lines 20–24)
– Text: “ ‘Dynamic pricing’ means pricing that fluctuates dependent on conditions where models retrain or recalibrate on information in near real-time, excluding promotional pricing offers…”
– AI relevance: Explicitly requires that pricing systems “retrain or recalibrate” on data as they operate—key characteristics of automated, data-driven AI models.
3. “Personalized algorithmic pricing” (§349-a.1(f), lines 4–7)
– Text: “ ‘Personalized algorithmic pricing’ means dynamic pricing derived from or set by an algorithm that uses consumer data…, which may vary among individual consumers or consumer populations.”
– AI relevance: Calls out the subset of dynamic (AI-driven) pricing that tailors prices to individuals by ingesting personal data.
4. “Consumer data” (§349-a.1(d), lines 17–19)
– Text: “ ‘Consumer data’ means any data that identifies or could reasonably be linked…with a specific natural person…, excluding location data.”
– AI relevance: Covers the personal information that most AI models use as features for segmentation or personalization.
Section B: Development & Research
This bill does not impose any requirements on AI R&D, data-sharing mandates, or funding rules. There are no explicit obligations on universities, labs, or startups to report models, share datasets, or adopt best-practices. All obligations target commercial use.
Section C: Deployment & Compliance
1. Mandatory Disclosure (§349-a.2, lines 11–17)
– Text: “Any person who knowingly advertises, promotes… personalized algorithmic pricing… shall include… a clear and conspicuous disclosure that states: ‘THIS PRICE WAS SET BY AN ALGORITHM USING YOUR PERSONAL DATA’.”
– Impact on sellers: E-commerce platforms, ride-hail services, airlines, dynamic-fare hotels, etc., will need UI changes wherever personalized prices appear.
– Impact on AI vendors: Third-party pricing-engine providers may need to supply hooks or flags in their APIs to signal to merchants when a price is AI-generated.
2. Exemptions (§349-a.4–5, lines 33–41)
– Text: Exempts insurers and financial services (e.g., “financial institutions… credit cards, personal loans, mortgages”).
– AI relevance: Carves out two major AI-powered pricing sectors (insurance underwriting models, credit-scoring models), so compliance load focuses on retail, travel, hospitality, gig platforms, etc.
Section D: Enforcement & Penalties
1. Injunction & Civil Penalty (§349-a.3, lines 17–32)
– Text: “The attorney general… may apply… to issue an injunction… and… may impose a civil penalty of not more than one thousand dollars for each violation.”
– AI systems risk: Each instance of non-disclosure (e.g., each pricing page view or each emailed offer) could theoretically be a separate “violation,” multiplying potential fines.
– Enforcement burden: Requires the AG to prove “knowing” use of algorithmic pricing without disclosure; ambiguous standards might spur litigation over what counts as “knowledge.”
2. Private Right of Action (via GBL §396.4(d), lines 16–22)
– Text: Amends GBL §396 to allow “any person aggrieved by a violation of subdivision three” (i.e., use of protected-class data) to bring suit under Executive Law §297.
– Note: This is separate but related—while §396 targets discrimination (see below), its private-action provision broadens enforcement beyond the AG to individuals or classes.
Section E: Overall Implications for the New York AI Ecosystem
1. Transparency drive: By mandating “THIS PRICE WAS SET BY AN ALGORITHM USING YOUR PERSONAL DATA” wherever individualized dynamic prices appear, the state forces AI systems into the open. Consumers will know when AI is at work.
2. Development slowdown risk: Startups building novel personalized-pricing algorithms may find decreased adoption if merchants fear the stigma or legal risk of “knowing” AI use, especially if penalties accumulate per‐instance.
3. Compliance costs: Even non-AI specialists (small retailers using off-the-shelf Shopify apps) must audit whether any pricing app “uses consumer data” to set prices and then modify storefronts accordingly.
4. Exempt pathways: Major AI-enabled financial and insurance pricing models escape these requirements, preserving existing innovation incentives in those sectors.
5. Ambiguities left:
– What exactly constitutes “using consumer data”? If an algorithm only ingests aggregate demand signals without individual identifiers, must the disclosure still appear?
– How to count “each violation”? Per page view? Per published ad? Per affected consumer?
– “Knowing” requirement: How much proof is needed that a merchant “knows” its pricing app uses personal data?
In sum, A.6765-A specifically targets AI-enabled dynamic pricing systems in consumer retail and hospitality, mandating prominent disclosures and risking per-instance fines for non-compliance. While it does not govern AI R&D or broader deployments, it could chill personalized pricing innovation among New York businesses and force significant compliance efforts.
Assembly - 6767 - Relates to artificial intelligence companion models
Legislation ID: 98295
Bill URL: View Bill
Sponsors
Assembly - 7029 - Relates to directing the commissioner of education to make recommendations to the board of regents regarding the incorporation of instruction in artificial intelligence system literacy into the school curriculum
Legislation ID: 98552
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an analysis of Assembly Bill A.7029 (2025-2026 session), which directs the New York State Commissioner of Education to develop recommendations for an AI literacy curriculum. Every point cites the bill’s exact language.
Section A: Definitions & Scope
1. “Artificial intelligence system” (AI system)
– Citation: “For purposes of this subdivision, the following definitions apply: (i) ‘Artificial intelligence system’ or ‘AI system’ means an engineered or machine-based system that, for explicit or implicit objectives, infers from the input it receives how to generate outputs that can influence physical or virtual environments.” (§2.f.i, lines 43–48)
– Analysis: This definition targets any software or machine that makes inferences from data and acts on them—broad enough to cover modern machine-learning models, rule-based expert systems, robotics, etc.
2. “AI literacy”
– Citation: “(ii) ‘AI literacy’ means the ability to understand, critically evaluate, and interact with artificial intelligence systems, including knowledge of their basic functioning, capabilities, limitations, and societal implications, and the skills to use them.” (§2.f.ii, lines 48–52)
– Analysis: By tying literacy to understanding both technological and ethical dimensions, the bill’s scope explicitly centers on human–AI interaction skills rather than on engineering or programming AI.
Section B: Development & Research
This bill does not address funding, data-sharing mandates, or research-specific reporting. Its focus is purely curricular. No clauses prescribe R&D incentives or requirements for researchers or startups.
Section C: Deployment & Compliance
Similarly, there are no provisions imposing certification, auditing, or liability rules on commercial AI deployments. The bill’s regulatory reach stops at K-12 education content:
– Curriculum content areas to recommend: “basic concepts and functioning of AI systems; critical evaluation of AI-generated content and AI system output; practical applications and limitations of AI; ethical considerations and societal impact; and safe and responsible interaction with AI systems.” (§2.a.(i)–(v), lines 1–6)
Section D: Enforcement & Penalties
The only “enforcement” steps relate to administrative deadlines and reporting—no penalties or compliance fines for non-education actors:
– Commissioner deadline: “No later than one hundred eighty days after the effective date of this subdivision, the commissioner shall provide a recommendation to the board of regents…” (§2.c, lines 21–27)
– Board of Regents vote: “shall vote to either accept or reject the commissioner’s recommendation no later than sixty days after receiving such recommendation.” (§2.c, lines 27–28)
– If rejected, mandatory report: “the commissioner shall provide a report … providing the reasons for such rejection not later than thirty days after the board of regents rejects such curriculum.” (§2.e, lines 37–42)
Section E: Overall Implications for New York’s AI Ecosystem
1. Workforce Preparation and Public Understanding
– By targeting K–12 pupils statewide, the bill aims to ensure broad, age-appropriate exposure to AI concepts and ethics. If adopted, it could shape public literacy such that future employees, consumers, and voters have a baseline understanding of AI.
2. Indirect Effects on AI Vendors and Startups
– A more AI-literate populace could drive demand for products that embed transparent, explainable AI—companies may need to adapt interfaces and documentation to suit newly informed users.
3. No Direct Impact on AI R&D or Regulation
– Because the bill imposes no research funding, data-governance, safety-audit, or liability framework, it neither accelerates nor restricts AI development efforts by universities, labs, or private companies.
4. Administrative Burden
– School districts and the State Education Department would incur some administrative costs (e.g., teacher training, curriculum development). The bill explicitly requires the commissioner to “consider the fiscal impact…on the state and school districts” (§2.b.iv–v, lines 15–19).
5. Ambiguities
– The bill leaves key specifics to future rulemaking—for instance, what counts as “safe and responsible interaction” (§2.a.v) or how much instructional time AI literacy should require (§2.b.iv). These definitional gaps grant the Board of Regents broad discretion at the implementation stage.
Summary
A.7029 is narrowly scoped to K–12 curriculum development. It defines “AI system” and “AI literacy,” sets deadlines for recommendation and adoption, requires stakeholder input (including from “the director of the state office of information technology services,” teachers, parents, and students (§2.b.i–iii)), and obligates transparent reporting if the curriculum is rejected. It does not establish any direct regulatory, funding, or enforcement mechanisms beyond the education sector.
Assembly - 7172 - Relation to the regulation of the use of artificial intelligence and facial recognition technology in criminal investigations
Legislation ID: 98704
Bill URL: View Bill
Sponsors
Assembly - 7656 - Enacts the "respect electoral audiovisual legitimacy (REAL) act"
Legislation ID: 111254
Bill URL: View Bill
Sponsors
Assembly - 768 - Enacts the "New York artificial intelligence consumer protection act"
Legislation ID: 54545
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. “Artificial intelligence decision system”
– Defined as “any computational process, derived from machine learning, statistical modeling, data analytics, or artificial intelligence, that issues simplified output … that is used to substantially assist or replace discretionary decision making for making consequential decisions that impact consumers.” (Sec 1550 § 2.)
– Relevance: targets algorithmic or model‐driven tools that automate or assist in decisions in consumer‐facing contexts.
2. “High-risk artificial intelligence decision system”
– Means “any artificial intelligence decision system that, when deployed, makes, or is a substantial factor in making, a consequential decision” (Sec 1550 § 10 (a)) but excludes narrow procedural tools and certain cybersecurity or back-office systems (Sec 1550 § 10 (b)(i–ii)).
– Relevance: narrows regulatory ambit to models affecting education, employment, lending, housing, healthcare, etc. (consequential decisions are defined in Sec 1550 § 4).
3. “General-purpose artificial intelligence model”
– “Any form of artificial intelligence decision system that … is capable of competently performing a wide range of distinct tasks” and can be integrated into downstream applications (Sec 1550 § 9 (a)).
– Excludes pre-release prototypes (Sec 1550 § 9 (b)).
4. “Algorithmic discrimination”
– Defined as differential treatment disadvantaging protected classes via AI systems (Sec 1550 § 1(a)).
– Excludes self-testing for compliance or historic redress, and private-club contexts (Sec 1550 § 1(b)).
5. Consequential decisions & protected sectors
– Consequential decisions include education, employment, financial, essential services, healthcare, housing, insurance, legal (Sec 1550 § 4).
– Protected classes mirror NY and federal civil-rights statutes (Sec 1550 § 1(a), § 3).
Section B: Development & Research
1. Required Developer Documentation (Sec 1551)
– Beginning Jan 1, 2027, developers of high-risk systems “shall use reasonable care to protect consumers from … risks of algorithmic discrimination” and must supply deployers with:
• Purposes, limitations, known harmful uses, training data summaries (Sec 1551 § 2(a–b));
• Bias-mitigation measures and monitoring instructions (Sec 1551 § 2(c–d));
• Model cards or dataset cards to enable deployer impact assessments (Sec 1551 § 3(a)).
– Potential impact: startups and open-source modelers must invest in documentation and third-party audits (Sec 1551 § 1(b) requires AG-approved auditors).
2. Technical Documentation for General-Purpose Models (Sec 1553)
– From Jan 1, 2027, developers “shall … create and maintain technical documentation” including training/testing processes and compliance evaluations, task scope, acceptable-use policies, versioning (Sec 1553 § 1(a)(i–ii)).
– Must supply integrators the means to understand capabilities and limitations (Sec 1553 § 1(b)(i–ii)).
– Exemptions if model is open-source with full parameter release or used solely internally (Sec 1553 § 2(a–b)).
– Researchers can test and build pre-market models without public disclosure (Sec 1550 § 9(b)).
Section C: Deployment & Compliance
1. Risk Management by Deployers (Sec 1552)
– Deployers of high-risk AI “shall use reasonable care …” with annual third-party bias audits (Sec 1552 § 1(a–b)).
– Must implement a risk management policy/program aligned to NIST AI Risk Management Framework or ISO/IEC 42001 (Sec 1552 § 2(a)(i)).
– Impact assessments before deployment and annually thereafter, covering purpose, data inputs/outputs, discrimination risks, mitigation steps, performance metrics, transparency measures, post-deployment monitoring (Sec 1552 § 3(a)(i)–(ii), § 3(b)(i)(A–G)).
– Exemptions if contract shifts duties to developer and system continues broad learning (Sec 1552 § 7).
2. Consumer Disclosure & Human-in-Loop Rights (Sec 1552 § 5)
– Pre-use notice that a high-risk AI “has been deployed,” purpose and nature of decision, contact info, and instructions to access additional statements (Sec 1552 § 5(a)).
– If decision is adverse, deployer must disclose reasons, AI’s role, data processed and source, and permit data correction and appeal with human review (Sec 1552 § 5(b)(i–ii)).
– General notice on website of all high-risk systems in use and data practices (Sec 1552 § 6).
3. Required Disclosure to All Users (Sec 1554)
– Any business that “deploys … any artificial intelligence decision system that is intended to interact with consumers” must disclose to each consumer “that such consumer is interacting with an artificial intelligence decision system.” (Sec 1554 § 1.)
Section D: Enforcement & Penalties
1. Exclusive AG enforcement & unfair trade practice (Sec 1556 § 1, § 5)
– Violations deemed “unfair trade practices” under § 349 GBL and enforced by the Attorney General; no private right of action (Sec 1556 § 4–5).
2. Notice & Cure Period (Sec 1556 § 2–3)
– Jan 1, 2027–Jan 1, 2028: AG must issue a 60-day cure notice before enforcement (Sec 1556 § 2).
– Post-2028: AG considers violation frequency, entity size, public harm, safety, cause when granting cure opportunity (Sec 1556 § 3).
3. Red-Teaming Defense (Sec 1556 § 6)
– Entities discovering violations via red-teaming can avoid liability if they cure within 60 days and adopt recognized AI risk frameworks (Sec 1556 § 6(a)(i–iii)).
4. Preemption & Exemptions (Sec 1555)
– Carves out systems approved by federal agencies (FDA, FAA), supervised financial institutions under equivalent regulation, certain defense or research contexts, HIPAA entities’ non-high-risk health AI (Sec 1555 § 4–5).
Section E: Overall Implications
• Advancement of Responsible AI: Mandated documentation, impact assessments, and risk management align with international best practices (NIST AI RMF, ISO/IEC 42001), potentially elevating AI governance among NY businesses and setting a state-level standard.
• Compliance Burden on Developers & Startups: Detailed disclosure, annual audit, and third-party reviews may raise costs for small AI firms and open-source contributors, though exemptions for internal or open-source models ease some strain.
• Consumer Protections & Transparency: Obligations to notify consumers of AI use and adverse decisions enhance transparency and user rights, potentially slowing automated processes but increasing trust.
• Regulatory Clarity & Enforcement by AG: Centralized enforcement under GBL § 349 provides a single point of contact but limits private suits; cure provisions and red-teaming defenses offer flexibility.
• Ambiguities & Risks:
– “Reasonable care” is not precisely defined and may vary by sector.
– Scope of “substantial factor” in decision making (Sec 1550 § 14) could capture any recommendation engine.
– Interaction disclosure exemption (“reasonable person would deem it obvious,” Sec 1554 § 2) may invite litigation over what is “obvious.”
Assembly - 773 - Relates to the use of automated decision tools by banks for the purposes of making lending decisions
Legislation ID: 54550
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused breakdown of New York’s proposed Banking Law § 103-a. Every point is tied directly to the bill text.
Section A: Definitions & Scope
1. “Automated decision tool” (§ 103-a 1(a), lines 6–14)
• “any computational process, derived from machine learning, statistical modeling, data analytics, or artificial intelligence, that issues simplified output…used to substantially assist or replace discretionary decision making for making lending decisions that impact natural persons.”
• Explicitly targets AI and related technologies (“machine learning…artificial intelligence”).
• Exempts basic IT (spam filters, spreadsheets, databases), making clear the focus is on decision-making systems.
2. “Disparate impact analysis” (§ 103-a 1(b), lines 17–24)
• “an impartial evaluation conducted by an independent auditor…testing of the extent to which use of an automated decision tool is likely to result in an adverse impact…on the basis of sex, race, ethnicity, or other protected class.”
• Focuses on bias testing—a core compliance exercise for AI fairness.
3. “Lending decision” (§ 103-a 1(c), line 25–26)
• Defined simply as “to screen applicants for a loan.”
• This ties the scope specifically to consumer loan underwriting.
Section B: Development & Research
• There are no provisions explicitly directed at AI research grants, data-sharing mandates, or model-development standards.
• However, by requiring annual “disparate impact analyses” (§ 103-a 2(a), lines 3–6), the bill indirectly compels banks and their AI vendors to invest in auditing processes, which may spur the growth of third-party auditing firms and fairness-testing tools.
Section C: Deployment & Compliance
1. Annual Bias Audits (§ 103-a 2, lines 2–9)
• “No less than annually, each bank that uses automated decision tools to make lending decisions shall: (a) conduct a disparate impact analysis…; and (b) submit a summary of the most recent disparate impact analysis…to the attorney general’s office.”
• Forces banks to operationalize fairness testing and to maintain audit trails.
2. Applicant Notice & Consent (§ 103-a 3(a)–(b), lines 10–24)
• Banks must notify each applicant “that an automated decision tool will be used,” disclose “characteristics…used,” data sources, retention policy, and—if denied—“the reason for such denial.” (§ 103-a 3(a)(i)–(iv), lines 12–20)
• Notice must occur “no less than twenty-four hours before the use” and allow the applicant to “opt out of or consent to such use and/or retention.” (§ 103-a 3(b), lines 21–24)
• This resembles GDPR-style transparency and consent rules, potentially slowing rapid AI deployments or requiring new consent-management infrastructure.
3. Data Correction & Appeal (§ 103-a 3(c), lines 25–31)
• “If an application…is denied based on personal information that is incorrect…the applicant…shall have thirty days to correct such information and appeal such denial.”
• Imposes a procedural remedy to incorrect data—banks may need to build workflows for appeals and data corrections.
Section D: Enforcement & Penalties
1. Attorney General Investigations (§ 103-a 4, lines 31–39)
• “The attorney general may initiate an investigation if a preponderance of the evidence…establishes a suspicion of a violation.”
• The AG can “mandate compliance with the provisions of this section or such other relief as may be appropriate.”
• No explicit monetary fines are listed, but AG actions can force remedial orders.
Section E: Overall Implications for NY’s AI Ecosystem
• Advances transparency and fairness in AI-powered lending by codifying bias audits and applicant rights.
• Raises compliance costs for banks and AI vendors: annual audits, notice/consent systems, appeal workflows.
• Could spur a new market for third-party auditors, fairness-testing toolkits, and consent-management platforms.
• May slow roll-out of opaque or continuously-learning underwriting models due to the 24-hour notice requirement and data-correction appeals.
• Empowers the Attorney General as the primary enforcer—banks and their AI partners will need to engage legal and compliance teams early in any deployment.
Ambiguities & Potential Interpretations
– “Substantially assist or replace discretionary decision making” (§ 103-a 1(a)): How much human oversight counts as “substantial”? Could cover scoring models with minimal human override.
– “Characteristics…used in the assessment” (§ 103-a 3(a)(ii)): It’s not clear whether this requires listing every feature (income, credit utilization) or just broad categories.
Overall, the bill zeroes in on AI-driven loan screenings—enforcing bias audits, transparency to applicants, and AG oversight—without regulating model development or research directly.
Assembly - 8295 - Relates to automated decision-making by government agencies
Legislation ID: 143680
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an analysis of A.8295-A (2025-2026), “Automated Decision-Making in Government Agencies,” organized as you requested. All quotations reference the printed bill (LBD11535-07-5).
Section A: Definitions & Scope
1. “Automated decision-making tool” (§501(1), pp.2–3)
• Text: “any software that uses algorithms, computational models, or artificial intelligence techniques … to automate, support, or replace human decision-making.”
• Relevance: This is the core AI definition. By naming “artificial intelligence techniques” alongside “algorithms” and “computational models,” the bill explicitly targets systems built on machine learning, deep learning, neural networks, or other AI-style approaches.
• Exclusions: The definition carves out “basic computerized processes” (e.g., “calculators, spellcheck tools, autocorrect functions, spreadsheets”) and “tools … that do not materially affect the rights, liberties, benefits, safety or welfare of any individual.” This limits the law to substantive decision-support or decision-replacement systems that have real-world impact.
2. “Meaningful human review” (§501(2), p.3)
• Text: “review, oversight and control … by one or more individuals who understand the risks, limitations, and functionality … and who have the authority to intervene or alter the decision … including … the ability to approve, deny, or modify any decision recommended or made by the automated tool.”
• Relevance: This term sets the bar for human-in-the-loop requirements, mandating not just a rubber-stamp but genuine oversight.
3. “Government agency” (§501(3), pp.3–4)
• Text: Broad list including “the state or civil division thereof; … county, city, town or village; … public authority, commission or public benefit corporation; or … any other public corporation … which exercises governmental power.”
• Relevance: Any public body in New York deploying AI-based decision systems falls under this article.
Section B: Development & Research
There are no direct provisions in this bill that fund or mandate data-sharing for AI R&D. The focus is entirely on transparency and risk assessment for tools already in (or proposed for) deployment.
Section C: Deployment & Compliance
1. Mandatory Disclosure (§502, pp.4–5)
• Text: “Any state agency that utilizes an automated decision-making tool … shall publish a list of such … tools on such state agency’s website … and annually thereafter.” (502:1–4)
• Impact: Creates public transparency. Startups and vendors must anticipate that their contracts with New York agencies will lead to public listing of their product and basic usage details.
2. Impact Assessments (§503, pp.5–8)
• Pre-deployment and biennial testing required: “An impact assessment shall be conducted prior to any material change … and at least once every two years.” (503:1, lines 31–39)
• Required elements (§503(1)(a)–(f)):
a. Objectives and design description (“summary of the underlying algorithms, computational models, and AI tools … design and training data used”) (503(1)(c), lines 49–54)
b. Fairness/bias testing (“testing for accuracy, fairness, bias and discrimination … and outlines mitigations”) (503(1)(d)(i), lines 55–56)
c. Cybersecurity & privacy risk analysis (503(1)(d)(ii), lines 1–4 on p.6)
d. Public health or safety risk (503(1)(d)(iii))
e. Misuse risk and safeguards (503(1)(d)(iv))
f. Data sensitivity and user control (503(1)(e))
g. Notification procedures for affected individuals (503(1)(f))
• Impact: These rigorous assessments resemble “algorithmic impact assessments” proposed in several jurisdictions. They will require vendors to supply sensitive model and data details and may increase costs and development lead times. Researchers may need to build explainability into their models to comply.
3. Cease-and-Desist on Biased Tools (§503(2), pp.8–9)
• Text: “If an impact assessment finds … discriminatory or biased outcomes, the government agency shall cease any utilization … of such automated decision-making tool, and of any information produced using such tool.”
• Impact: A powerful stop-work requirement. Agencies (and indirectly vendors) must remediate bias or withdraw the tool. This raises the stakes on fairness testing.
4. Publication & Redaction (§504, pp.9–11)
• Text: “Each impact assessment … shall be submitted to the governor … thirty days prior to … implementation.” (504(1), lines 30–34)
• Public posting with limited redactions “if disclosure … would jeopardize … security … infringe privacy … or impair … IT or operational assets” (504(2)(b), lines 37–44)
• Security tech carve-out (504(2)(c), lines 45–54) for fraud detection, incident response, etc.
• Impact: Balances transparency against legitimate confidentiality/security concerns. Agencies must still publish an “explanatory statement” about any redactions.
5. Statewide Inventory (§103-f, pp.11–13)
• Text: “The office shall maintain an inventory of state automated decision-making tools … posted on the New York state open data website … annually.” (103-f(1), lines 1–8)
• Impact: Creates a single, publicly searchable catalog of every AI-style tool used across state government. Startups can discover potential customers; watchdogs can audit compliance.
6. Initial Retrospective Disclosure (§3, pp.13–14)
• Text: “Any government agency … that … utilizes an automated decision-making tool … shall submit to the legislature a disclosure … no later than one year after the effective date … including … (a) description, (b) vendor list, (c) start date, (d) purpose and human discretion supplanted, (e) impact assessment history, (f) any other relevant information.” (Sec.3, lines 24–40)
• Impact: Forces agencies to catalogue and report legacy AI tool use.
Section D: Enforcement & Penalties
• The only explicit enforcement mechanism is in §503(2)—mandatory suspension if bias is found. No civil or criminal penalties are specified.
• Implicitly, an agency’s failure to comply with publication or assessment requirements could be challenged via FOIL or judicial mandamus, but the bill does not create fines.
Section E: Overall Implications
1. Increased Transparency and Oversight—By requiring public disclosure, detailed impact assessments, and a centralized inventory, New York aims to shine a light on government AI. This will likely slow down procurement and deployment but raise public trust.
2. Higher Vendor Burden—Companies selling AI systems to New York agencies must furnish architecture details, training data schemas, fairness metrics, and remediation plans. Smaller vendors and open-source tool providers may struggle to meet confidentiality and IP concerns.
3. Research Impact—Researchers partnering with state agencies will need to plan for reproducibility, explainability, and rigorous bias testing from the outset. This could align with best practices but also increase project overhead.
4. End-User Protection—The “meaningful human review” requirement and bias cessation clause place citizen rights and anti-discrimination at the forefront, ensuring AI decisions can be overturned and biased systems removed.
5. Regulatory Precedent—New York’s model could become a template for other states, establishing a de facto standard for government use of AI. Its lack of explicit penalties, however, may limit enforceability without further legislation.
Ambiguities & Open Questions
– “Material change” triggering reassessment (§503(1), lines 40–42): It is unclear what qualifies as “material”—a model retraining? A new hyperparameter setting? Draft regulations or guidance will be needed.
– Scope of “security” redactions (§504(2)(c)): Agencies could interpret “malicious activity” broadly and redact large swaths of assessment, diluting transparency.
In sum, A.8295-A creates a detailed transparency and risk-management regime for AI-style tools in New York government. It does not directly regulate private-sector AI but may shape the broader market by setting high standards for fairness testing, documentation, and human oversight.
Assembly - 8523 - Requires certain political communications to include provenance data for all audio, images or videos used in such communications
Legislation ID: 147753
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of the “Election Content Accountability Act” (A.8523), organized as requested. Every claim cites the bill text directly.
Section A: Definitions & Scope
1. “Provenance data” (§14-106(8)(a)(i), lines 6–17)
• “Provenance data” is defined as metadata “that records the origin or history of digital content…, and which discloses: (1) information about the origin or creation of the content; (2) any subsequent editing or modification…; and (3) any use of generative artificial intelligence in generating or modifying the content.”
• AI relevance: explicitly calls out “use of generative artificial intelligence” as a provenance element.
2. “Generative artificial intelligence system” (§14-106(8)(a)(ii), lines 18–24)
• Defined as “a class of AI model that is self-supervised and emulates… input data in order to generate derived synthetic content, including… images, videos, audio, text, and other digital content.”
• AI relevance: directly targets so-called “foundation” or generative models.
3. “Synthetic content” (§14-106(8)(a)(iii), lines 1–4)
• Defined as “audio, images or videos that have been produced or significantly modified by a generative artificial intelligence system.”
• AI relevance: distinguishes AI-generated or AI-modified media from human-created media.
4. “AI model” (§14-106(8)(a)(iv), lines 4–6)
• Broadly defined as any “information system… that implements artificial intelligence technology and uses computational, statistical, or machine-learning techniques to produce outputs from a given set of inputs.”
• AI relevance: all-encompassing definition that could include narrow ML models as well as large generative systems.
5. “Generative artificial intelligence provider” (§14-106(8)(a)(v), lines 8–12)
• Means “an organization or individual that creates, codes, substantially modifies, or otherwise produces a generative artificial intelligence system… made publicly available for use by a New York resident.”
• AI relevance: captures both open-source and commercial model creators, regardless of compensation.
Scope Statement
• Applies to “campaigns for the office of governor, lieutenant governor, attorney general, or comptroller” beginning with the 2030 election (subdiv. 8(b), lines 13–18).
• Expires December 31, 2030 (Section 4, lines 46–49).
Section B: Development & Research
– The bill contains no direct provisions on AI research funding, reporting, or data-sharing mandates.
– However, the requirement to “apply provenance data… either directly or through the use of third-party technology” (§14-106(8)(b), lines 15–18) may spur demand for provenance-tool R&D.
• Ambiguity: “third-party technology” is undefined—could be proprietary or open-source systems.
Section C: Deployment & Compliance
1. Provenance-tagging requirement (§14-106(8)(b)–(c), lines 13–32)
• “A campaign… shall apply provenance data… to all political communications… that are produced as or include images or videos.”
• Minimum data required (§14-106(8)(c), lines 19–27):
i. “Type of device, system, or service… used to generate” the media;
ii. “Specific portions… that are synthetic content”;
iii. “Whether the content was created or edited using artificial intelligence”;
iv. “Name of the generative artificial intelligence provider”;
v. “Time and date any of the provenance data… was applied.”
Potential Effects
• Campaigns must integrate AI-aware tooling in their media pipelines.
• Startups building “content credential” or “provenance” SDKs may see a new market.
• Established vendors (e.g., content-authenticity platforms) will be tapped to certify compliance.
• Ambiguity remains around the exact format or embedded location of the provenance metadata—delegated to AG rule-making (§3, lines 39–45).
Section D: Enforcement & Penalties
• Intentional or grossly negligent violations: up to $100,000 per violation (§14-106(8)(d), lines 33–38).
• Unintentional or non-grossly negligent violations: up to $50,000 per violation.
• Enforcement by the New York Attorney General (Section 3, lines 39–45).
Implications of Enforcement
• Campaigns and their consultants will need compliance audits.
• Potential chilling effect on rapid deployment of synthetic content in campaigns.
Section E: Overall Implications
1. Advances AI-certification/tools market
– By mandating provenance metadata, the bill creates demand for content-credential solutions.
2. Restricts opaque AI content in political ads
– Forces transparency about which portions of ads are AI-generated.
3. Regulatory burden on campaigns
– Smaller campaigns may struggle with added technical and legal compliance costs.
4. Regulatory model for other sectors
– Though narrowly targeted at political communications, this approach could serve as a template for provenance requirements in news media, advertising, or other regulated contexts.
5. Sunset clause limits long-term impact
– Expires at end of 2030, suggesting a pilot-style approach.
Ambiguities & Open Questions
– “Third-party technology” and acceptable “methods, formats” are undefined until AG rule-making.
– How granular must the “specific portions” of synthetic content be identified? Frame-level? Clip-level?
– Treatment of audio-only or text-only AI content is out of scope.
In sum, this bill explicitly targets generative AI in political media by defining key AI terms, mandating provenance tagging, and imposing significant penalties for non-compliance. It does not address AI research or wider commercial deployment beyond the campaign context, but it does lay groundwork for an AI-provenance ecosystem in New York.
Assembly - 8546 - Relates to requiring disclosure of use of generative artificial intelligence in a civil action
Legislation ID: 147779
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of Assembly Bill A.8546 (2025–2026), which amends New York’s Civil Practice Law and Rules to require disclosure whenever generative AI is used to draft court papers or appellate briefs.
Section A: Definitions & Scope
1. “Generative artificial intelligence” is defined broadly in new Rule 2107(b). Each of the five sub-paragraphs targets AI systems capable of creating or assisting with content generation.
• “any artificial system that performs tasks under varying and unpredictable circumstances without significant human oversight, or that can learn from experience and improve performance when exposed to data sets;” (Rule 2107(b)(1), lines 14–17)
• “an artificial system developed in computer software, physical hardware, or other context that solves tasks requiring human-like perception, cognition, planning, learning, communication, or physical action;” (Rule 2107(b)(2), lines 17–19)
• “an artificial system designed to think or act like a human, including cognitive architectures and neural networks;” (Rule 2107(b)(3), lines 20–21)
• “a set of techniques, including machine learning, that is designed to approximate a cognitive task; and/or” (Rule 2107(b)(4), lines 22–23)
• “an artificial system designed to act rationally, including an intelligent software agent or embodied robot that achieves goals using perception, planning, reasoning, learning, communicating, decision making, and acting.” (Rule 2107(b)(5), lines 1–4 on p. 2)
These definitions explicitly encompass generative AI tools (e.g. large language models), neural networks, and other machine-learning–based content generators. By including both software-only and embodied systems, the rule aims for maximal coverage.
2. “Filing produced using generative artificial intelligence” is in scope only if the AI was “used in the drafting” of any paper or file. If no generative AI was used, “no disclosure is required under this rule.” (Rule 2107(c), lines 5–7)
Section B: Development & Research
– The bill contains no provisions funding or directing AI R&D, data sharing, or reporting requirements on researchers. Its focus is strictly on gatekeeping of court filings.
Section C: Deployment & Compliance
1. Disclosure requirement. Any court paper “drafted with the assistance of generative artificial intelligence must attach to the filing a separate affidavit disclosing such use and certifying that a human being has reviewed the source material and verified that the artificially generated content is accurate.” (Rule 2107(a), lines 3–8)
– Impact on law firms and vendors: Firms will need internal workflows to tag when AI tools assist in drafting and to secure human verification affidavits. AI tool vendors may have to provide audit trails or usage logs to support affidavits.
– End users (clients) may face higher billing if attorneys pass on the time and costs for affidavit preparation and compliance.
2. Appellate briefs. The bill amends Rule 5528(a) to add a new paragraph 6 requiring the same disclosure for briefs:
“6. if required by rule 2107, a disclosure of the use of generative artificial intelligence in the drafting of the brief and certification that the content therein was reviewed and verified by a human.” (Rule 5528(a)(6), lines 22–24)
Section D: Enforcement & Penalties
– The act itself does not specify new monetary penalties or criminal sanctions for nondisclosure. Enforcement would rely on existing CPLR mechanisms—motions to strike, sanctions for frivolous or misleading filings, and contempt powers. The absence of a specific penalty clause injects ambiguity as to how strictly courts will enforce the rule or what sanctions they may impose.
Section E: Overall Implications
• Transparency. Forces disclosure of AI-assisted drafting, which may deter overreliance on generative tools and preserve professional accountability.
• Administrative burden. Introduces procedural steps (affidavits, human verifications) that could slow down filings and increase costs—particularly for high-volume litigators or firms experimenting with AI.
• Market effects. May favor established vendors who can provide compliance support over smaller startups lacking audit-trail capabilities.
• Regulatory precedent. Sets a template for other disclosure mandates in administrative or legislative contexts, potentially expanding beyond civil litigation.
Ambiguities & Interpretations
– “Reviewed the source material” could be interpreted narrowly (verifying every citation) or broadly (spot-checking).
– Courts have discretion in defining “assistance” by AI: does mere grammar-checking or formatting count?
By anchoring on Rule 2107’s disclosure and certification obligations, this bill reshapes how generative AI tools can be deployed in New York civil practice without criminalizing their use—rather, it layers on transparency and human-in-the-loop verification.
Assembly - 8556 - Relates to the use of an artificial intelligence, algorithm, or other software tool for the purpose of utilization review
Legislation ID: 147789
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a structured analysis of A.8556’s AI-related provisions. All citations refer to the bill’s Section, subdivision or paragraph, and line‐ranges.
Section A: Definitions & Scope
1. “Artificial intelligence” defined (sec. 1, subd. 3 [lines 34–38]):
– Quotation: “For purposes of this section, ‘artificial intelligence’ means an engineered or machine-based system … that can, for explicit or implicit objectives, infer from the input it receives how to generate outputs…”
– Analysis: This is the sole definition in both the Public Health Law and Insurance Law amendments. It is broad and covers any system with autonomy and inference capability. It makes clear that the rules apply only to “AI” as thus defined.
2. Applicability to “utilization review” (sec. 1, subd. 4 [lines 39–42]; sec. 2, subd. 4 [lines 12–14]):
– Quotation: “This section shall apply to utilization reviews that prospectively, retrospectively, or concurrently review requests for covered health care services.”
– Analysis: Limits the AI rules to a narrow use case—insurance or Medicaid/Medicare utilization review—rather than all AI in health care or beyond.
Section B: Development & Research
There are no provisions in this bill addressing AI research funding, data sharing for R&D, or reporting of developmental practices. All requirements relate to use of existing AI tools in utilization review, not to their development.
Section C: Deployment & Compliance
1. Data inputs & decision criteria (sec. 1, subd. 1(a)–(c) [lines 12–23]; identical in sec. 2, subd. 1(a)–(c)):
– Quotation:
• “(a) … bases its determination on … (i) an enrollee’s medical … history; (ii) individual clinical circumstances …; (iii) other relevant clinical information …”
• “(b) … does not base its determination solely on a group dataset.”
– Analysis: AI systems must be patient-specific and cannot rely only on population-level statistics. This could restrict vendors whose models primarily leverage aggregate claims data.
2. Non-supplanting of provider decision-making (sec. 1, subd. 1(d) [lines 1–4]; sec. 2, subd. 1(d) [lines 29–32]):
– Quotation: “The artificial intelligence … does not supplant health care provider decision-making.”
– Analysis: The bill explicitly prohibits fully automated adjudication without human clinical oversight, ensuring that licensed professionals retain final authority.
3. Non-discrimination & equitable application (sec. 1, subd. 1(e)–(f) [lines 4–9]; sec. 2, subd. 1(e)–(f) [lines 31–37]):
– Quotation:
• “(e) … does not discriminate, directly or indirectly, against enrollees in violation of state or federal law.”
• “(f) … is fairly and equitably applied, including in accordance with any applicable regulations and guidance issued by the federal department of health and human services.”
– Analysis: Vendors must test models for bias and align with HHS guidance. Startups may face additional compliance costs for bias audits.
4. Transparency & auditability (sec. 1, subd. 1(g)–(h) [lines 11–15]; sec. 2, subd. 1(g)–(h) [lines 38–44]):
– Quotation:
• “(g) … open to inspection for audit or compliance reviews by the department.”
• “(h) Disclosures … are contained in the written policies and procedures, as required by section 4902 of this title.”
– Analysis: Insurers must maintain documentation of model logic, performance metrics, and oversight processes. This could spur development of third-party audit services.
5. Ongoing performance review (sec. 1, subd. 1(i) [lines 16–18]; sec. 2, subd. 1(i) [lines 45–47]):
– Quotation: “The ... tool’s performance, use, and outcomes are periodically reviewed and revised to maximize accuracy and reliability.”
– Analysis: Periodic re-validation is mandated; vendors will need to build monitoring pipelines and update models.
6. Data use limits & HIPAA (sec. 1, subd. 1(j) [lines 19–22]; sec. 2, subd. 1(j) [lines 48–51]):
– Quotation: “… Patient data is not used beyond its intended and stated purpose, consistent with this section and the federal Health Insurance Portability and Accountability Act …”
– Analysis: Reinforces HIPAA compliance and prohibits secondary uses (e.g., marketing), constraining data monetization.
7. Prohibition on causing harm (sec. 1, subd. 1(k) [lines 23–24]; sec. 2, subd. 1(k) [lines 52–53]):
– Quotation: “The ... tool does not directly or indirectly cause harm to the enrollee.”
– Analysis: Vague “harm” standard could lead to uncertainty; insurers may demand indemnities from AI vendors.
8. Final medical necessity determination by humans (sec. 1, subd. 2 [lines 24–34]; sec. 2, subd. 2 [lines 53–5 6]):
– Quotation:
“Notwithstanding subdivision one… the artificial intelligence … shall not deny, delay, or modify health care services based … on medical necessity. A determination of medical necessity shall be made only by a licensed physician or … health care professional … by reviewing … provider’s recommendation, the enrollee’s medical … history, and individual clinical circumstances.”
– Analysis: AI may only assist, not dictate, the final coverage decision. This preserves clinician authority but may reduce efficiency gains touted by full automation.
9. Alignment with federal HHS rules (sec. 1, subd. 5 [lines 42–49]; sec. 2, subd. 5 [lines 15–23]):
– Quotation: “A health care service plan subject to this section shall comply with applicable federal rules and guidance … The department may issue guidance … within one year of … federal guidance … Such guidance shall not be subject to the state administrative procedure act.”
– Analysis: New York defers to forthcoming federal AI regulations in health care, avoiding duplication but also creating potential uncertainty until federal rules arrive.
10. Contracting and procurement exemptions (sec. 1, subd. 6 [lines 51–56]; sec. 2, subd. 6 [lines 24–29]):
– Quotation: “The department may enter into exclusive or nonexclusive contracts … exempt from articles 9 and 11 of the state finance law, and shall not be subject to review or approval of any other state agency or entity.”
– Analysis: Eases state’s ability to procure AI compliance or audit services, but raises transparency concerns for procurement.
Section D: Enforcement & Penalties
– Enforcement is delegated to the state Department of Health or Department of Financial Services via “inspection for audit or compliance reviews” (sec. 1, subd. 1(g); sec. 2, subd. 1(g)) and through issuance of guidance (sec. 1, subd. 5; sec. 2, subd. 5).
– No explicit civil or criminal penalties are stated. Non-compliance presumably triggers sanctions under the broader utilization review statutes (e.g., plan sanctions or fines), but the bill does not specify.
Section E: Overall Implications
1. Startups & vendors will face new transparency, data governance, and human-in-the-loop requirements. These raise development costs (bias audits, logging, documentation) and may chill entry of smaller AI innovators.
2. Established insurers must build or buy AI oversight capabilities; the procurement exemptions speed contracting but reduce procedural safeguards.
3. Clinicians retain medical necessity authority, potentially limiting AI’s efficiency benefits in utilization review workflows.
4. Regulators gain broad audit powers and can issue binding guidance outside the Administrative Procedure Act, accelerating rulemaking but reducing public comment.
5. By deferring to HHS guidance, New York avoids premature rules but inherits federal delays or gaps.
In sum, A.8556 does not foster AI innovation directly but tightly governs AI’s role in utilization review—prioritizing patient-specific adjudication, human oversight, and non-discrimination over full automation. Researchers and developers focusing on clinical decision support may need to tailor solutions to these transparency, audit, and human review mandates.
Assembly - 8595 - Enacts the "New York artificial intelligence transparency for journalism act"
Legislation ID: 148212
Bill URL: View Bill
Sponsors
Assembly - 8833 - Establishes understanding artificial intelligence responsibility act
Legislation ID: 166783
Bill URL: View Bill
Sponsors
Senate - 1169 - Relates to the development and use of certain artificial intelligence systems
Legislation ID: 66847
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused breakdown of S.1169, the proposed “New York Artificial Intelligence Act.” All citations refer to the numbered sections, subsections or lines of the bill as introduced.
Section A: Definitions & Scope
1. “Artificial intelligence system” / “AI system” (§ 85.2, lines 55–3)
• “AI system means a machine-based system … infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.”
• Exclusion: “Artificial intelligence shall not include any software used primarily for basic computerized processes … and that do not materially affect the rights, liberties, benefits, safety or welfare of any individual within the state.”
Relevance: Explicitly targets software that learns or adapts to make consequential outputs; carves out traditional software tools.
2. “High-risk AI system” (§ 85.12, lines 15–19)
• Defined as any AI system that “is a substantial factor in making a consequential decision” or “will have a material impact on the … rights, civil liberties, safety, or welfare of an individual in the state.”
• Connects to “Consequential decision” (§ 85.4), which covers employment, education, credit, healthcare, law enforcement, etc.
Relevance: Signals the core regulatory focus on systems affecting fundamental rights.
3. “Algorithmic discrimination” (§ 85.1, lines 35–45)
• Any condition where use of an AI system “contributes to unjustified differential treatment or impacts … based on … protected class.”
• Exempts bona fide testing to identify bias and certain diversity remediation (§ 85.1(a)–(b)).
Relevance: Establishes the civil-rights rationale for oversight and prohibition.
4. “Developer” vs. “Deployer” (§ 85.5–7, lines 47–56)
• “Developer” creates or substantially modifies an AI system.
• “Deployer” uses or makes available an AI system to the public in New York.
Relevance: Assigns parallel obligations to both creators and users of AI systems.
Section B: Development & Research
Although no direct R&D funding is mandated, the bill imposes transparency/reporting on model development:
1. Training data disclosures (§ 88.3(c), lines 49–56)
• Requires “datasheets comprehensively describing the datasets upon which models were trained and evaluated, how and why datasets were collected, how that training data will be used and maintained going forward.”
Effect: Startups and research groups must document data lineage, which can slow rapid prototyping or increase overhead.
2. Internal risk assessments (§ 88.3(g)(iii) & § 88.4(e)(v), lines 17–25 & 44–52)
• Developers/deployers must submit “documentation and results of testing … to identify all reasonably foreseeable risks related to algorithmic discrimination, accuracy and reliability, privacy and autonomy, and safety and security.”
Effect: Encourages systematic risk management in research, possibly requiring in-house compliance teams.
3. Risk management policy (§ 89.1, lines 20–29)
• Must “plan, document, and implement a risk management policy and program … to identify, document, and mitigate known or reasonably foreseeable risks of algorithmic discrimination.”
• Must align with NIST AI Risk Management Framework (v1.0) or later more stringent versions.
Effect: Aligns research practices with recognized standards, potentially raising quality but also raising barriers for small teams.
Section C: Deployment & Compliance
1. Prohibition of biased high-risk AI (§ 86.1–2, lines 32–41)
• “Unlawful discriminatory practice: for a developer or deployer to use, sell, or share a high-risk AI system … that produces algorithmic discrimination” or that “has not passed an independent audit.”
Effect: Formalizes liability for biased outcomes; may deter deployment or encourage pre-release auditing.
2. Notice & opt-out (§ 86-a.1, lines 42–56)
• Deployers must “inform the end user at least five business days prior to the use … in clear, conspicuous, and consumer-friendly terms” and allow users to opt-out for a human decision instead.
• Option to waive 5-day notice for some benefit decisions (§ 86-a.1(b)–(c)).
Effect: Imposes consumer-rights notices akin to consent regimes; startups must build user interfaces for these notices.
3. Appeals & human review (§ 86-a.2, lines 17–25)
• After an automated consequence, deployers must “provide and explain a process … to (a) formally contest the decision, (b) provide information …, and (c) obtain meaningful human review.”
Effect: Forces human-in-the-loop fallback, slowing fully automated systems in sensitive domains.
4. Audits (§ 87, lines 24–56)
• Third-party audits required pre-deployment, six months post-deployment, and every 18 months thereafter.
• Audits must cover “disparate impacts,” “system accuracy,” data-security compliance, and conformity with the risk management program.
• Auditors must be independent (no other services in past 12 months; no contingent fees).
Effect: Creates recurring compliance costs; may benefit audit firms.
5. Reporting (§ 88, lines 24–56 & 4/1–18)
• Developers/deployers must file a report with the Attorney General before deployment and annually (or after any “substantial change”), including audit copies and legal attestation of compliance or remediation plan.
• Reports detail software stacks, uses, limitations, monetization, risk assessments and monitoring plans.
Effect: High transparency for regulators and public; may discourage secretive R&D but improve accountability.
6. Ban on social scoring (§ 89-a, lines 1–12)
• “No person … shall develop, deploy, use, or sell an AI system which evaluates or classifies the trustworthiness of natural persons … with the social score leading to … differential treatment.”
Effect: Aligns with EU’s AI Act provisions; blocks potential face-recognition or credit scoring uses of social media behavior.
Section D: Enforcement & Penalties
1. Attorney General enforcement (§ 89-b.1, lines 14–27)
• AG may seek injunctions and civil penalties “not more than twenty thousand dollars for each violation.”
• No need to show actual harm.
2. Private right of action (§ 89-b.2, lines 29–32)
• Any person harmed may sue for “compensatory damages and legal fees.”
• Presumption of violation at motion to dismiss (§ 89-b.3, lines 33–38), rebuttable only by “clear and convincing evidence.”
Effect: Strong deterrent for developers/deployers; risk of litigation may chill innovation or favor incumbents with legal teams.
3. Whistleblower protections (§ 86-b, lines 37–46)
• Prohibits retaliation against employees who report violations to the AG; requires internal anonymous reporting channels.
Effect: Encourages internal audits and compliance.
Section E: Overall Implications
• Innovation vs. Guardrails: The bill requires extensive pre- and post-deployment compliance (audits, reporting, risk management) that raise costs especially for small teams. Established vendors may absorb these but startups and academic labs will face higher barriers to market.
• Civil-rights focus: By defining “high-risk AI” around “consequential decisions,” the law concentrates on systems that materially affect people’s lives, aligning AI oversight with anti-discrimination law.
• Transparency & Accountability: Public database of reports (AG to “maintain an online database … accessible to the general public,” § 88.5(b)) will enable civil society scrutiny.
• Regulatory Ecosystem: The AG gains broad rule-making, enforcement, and injunction powers. The private right of action and presumptions at pleading stage intensify legal risk.
• Model for Other States: Drawing on NIST’s AI Risk Management Framework and echoing EU’s AI Act, New York positions itself as a national leader in AI governance, potentially influencing federal policy or interstate compacts.
Ambiguities Noted
– “Substantial factor” (§ 85.15) is phrased broadly and could sweep in systems with any AI-derived output.
– “Reasonably foreseeable uses” (§ 88.3(a)(iv), § 88.4(a)(iv)) may require speculation about remote use-cases.
– The 45-day deadline for human decisions (§ 86-a.1) may conflict with regulatory timeframes in other domains (e.g. financial services).
In sum, S.1169 would impose one of the most comprehensive AI regulatory regimes in the U.S., balancing civil-rights protection and consumer notice requirements against increased compliance obligations for AI creators and users.
Senate - 1815 - Requires publishers of books created with the use of generative artificial intelligence to contain a disclosure of such use
Legislation ID: 67665
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of New York Senate Bill S.1815 (2025-2026), organized into Sections A through E per your instructions. All quotations cite section and line numbers from the bill text.
Section A: Definitions & Scope
1. “Generative artificial intelligence” (lines 12–17):
– Citation: “For the purposes of this section, ‘generative artificial intelligence’ shall mean the use of machine learning technology, software, automation, and algorithms… including, but not limited to:” (§338 3, lines 12–14).
– Analysis: This is an explicit, broad definition of AI that covers both software and hardware systems “that learns from experience” (§338 3(a), lines 17–19) and “cognitive architectures and neural networks” (§338 3(c), lines 1–3). By listing five illustrative categories (3(a)–(e)), the bill targets virtually any system that automates or simulates human‐like tasks.
2. “Books subject to the provisions” (lines 8–11):
– Citation: “Books subject to the provisions of this section shall include… all printed and digital books… consisting of text, pictures, audio, puzzles, games or any combination thereof.” (§338 2, lines 8–11).
– Analysis: This scope clause expressly covers both print and e-books, regardless of medium or target audience. The breadth ensures that any consumer‐facing book product using generative AI falls under the disclosure rule.
Section B: Development & Research
– The bill contains no provisions that directly address AI research funding, reporting, data sharing, or collaboration. It is limited solely to a labeling requirement for consumer publications.
– Ambiguity note: No carve-outs for academic publications or research monographs. In principle, “books… published in this state” (§338 1, lines 4–7) could include university press volumes, though enforcement focus will likely be commercial publishers.
Section C: Deployment & Compliance
1. Mandatory Disclosure (lines 4–7):
– Citation: “Any book that was wholly or partially created through the use of generative artificial intelligence… shall conspicuously disclose upon the cover of the book, that such book was created with the use of generative artificial intelligence.” (§338 1, lines 4–7).
– Analysis: Publishers must label products as AI‐generated if generative AI was used “wholly or partially.” “Conspicuously disclose upon the cover” implies a visible notice akin to a label or tagline. Failure to comply could expose publishers to general business law enforcement, though no specific penalty is described.
2. Potential impact on publishers and vendors:
– Established publishers will need to audit their production workflows to determine where AI tools were used (e.g., cover-design algorithms, text-completion services).
– Startups offering AI-assisted writing tools may see increased demand for compliance certifications or self-reporting modules.
Section D: Enforcement & Penalties
– The bill does not specify enforcement mechanisms or penalties within §338. It amends the general business law but omits any new civil or criminal penalties.
– Implicit enforcement: The New York Department of State or Attorney General’s office could treat non-compliance as an “unfair or deceptive act” under existing business-law statutes, but this is not spelled out.
– Ambiguity note: The absence of explicit fines or injunction procedures creates uncertainty. Publishers may lack clarity on what constitutes a violation and the magnitude of potential sanctions.
Section E: Overall Implications
1. Transparency and Consumer Awareness:
– By mandating cover disclosures, the bill aims to inform readers when AI played a role in content creation. This could decrease perceived value of AI-generated works or shift market preferences toward human-authored books.
2. Compliance Costs and Administrative Burden:
– Publishers (large and small) will need internal compliance checks, possibly adding staff or tools to track AI usage in writing, editing, illustration, or layout.
3. Innovation Effects:
– Startups may develop compliance solutions (e.g., logging tools that tag AI-generated segments). Conversely, some innovators might avoid offering AI-generated “books” in New York to escape disclosure hassles.
4. Regulatory Gaps:
– The bill does not address non-book applications of generative AI (e.g., journalism, marketing materials) nor does it establish a broader AI oversight framework.
– Without clear penalties or enforcement guidance, the practical impact may remain limited to large publishers wary of litigation risk.
In summary, S.1815 introduces a narrowly focused transparency requirement for books using generative AI. It defines “generative artificial intelligence” broadly (§338 3) and requires a cover notice (§338 1), but leaves enforcement and penalties to existing general business-law authorities. The measure is unlikely to shape AI research directly but will prompt publishers and AI-tool vendors to adapt processes to ensure proper labeling.
Senate - 1962 - Enacts the "New York artificial intelligence consumer protection act"
Legislation ID: 67845
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. “Artificial intelligence decision system” (Def. §1550.2, lines 11–16)
– “any computational process, derived from machine learning, statistical modeling, data analytics, or artificial intelligence, that issues simplified output … used to substantially assist or replace discretionary decision making for making consequential decisions that impact consumers.”
– Relevance: This expansive definition explicitly targets AI‐powered tools that make or influence high‐stakes decisions. It covers both bespoke models and “general‐purpose” AI integrated into downstream apps.
2. “High-risk artificial intelligence decision system” (Def. §1550.10, lines 1–4)
– “any artificial intelligence decision system that … makes, or is a substantial factor in making, a consequential decision.”
– Relevance: Focuses regulation on AI where errors or biases can have major legal or economic effects (e.g., hiring, lending).
3. “General-purpose artificial intelligence model” (Def. §1550.9, lines 46–53)
– “any form of artificial intelligence decision system that (i) displays significant generality; (ii) is capable of competently performing a wide range of distinct tasks; and (iii) can be integrated into a variety of downstream applications or systems.”
– Relevance: Captures LLMs and foundation models, not just narrow algorithms.
4. “Algorithmic discrimination” (Def. §1550.1(a), lines 16–23)
– “any condition in which the use of an AI decision system results in … differential treatment or impact … on the basis of … protected class.”
– Relevance: Grounds the entire act in civil‐rights law, tying AI output to discrimination prohibitions.
Section B: Development & Research
1. Required Documentation by Developers (§1551.2, lines 38–48)
– “Beginning … 2027, a developer … shall make available … (a) statement … foreseeable uses … known harmful … uses; (b) documentation … summaries of data used to train … limitations … risks of algorithmic discrimination … purpose … intended benefits; (c) evaluation and mitigation measures … pre-deployment auditing; (d) instructions on how AI should be used and monitored.”
– Impact: Forces transparency from AI startups and researchers about training data, risks, and intended scope—could slow prototyping but improve third‐party auditability.
2. Exemptions for R&D (§1555.1(h), lines 21–28)
– “(h) conduct research, testing, and development … before such AI decision system … is placed on the market, deployed, or put into service.”
– Impact: Carves out a “safe harbor” for in‐lab experiments and red‐teaming so long as models aren’t publicly released, preserving early‐stage innovation.
3. Technical Documentation for Foundation Models (§1553.1, lines 36–44)
– “Each developer of a general-purpose AI model shall … (a)(i) include training and testing processes and evaluation results …; (ii) tasks intended; integration targets; acceptable use policy; release date; distribution methods; I/O modalities; (iii) review annually. And (b) provide integration documentation to downstream developers.”
– Impact: Imposes new overhead on teams building large models—needs versioned “model cards” and continuous updates but also sets state‐level best practices mirroring NIST.
Section C: Deployment & Compliance
1. Risk Management by Deployers (§1552.2(a), lines 17–30)
– “Beginning … 2027, each deployer … shall implement and maintain a risk management policy and program … an iterative process … governed by NIST AI Risk Management Framework or ISO/IEC 42001 … tailored to size, complexity, data sensitivity.”
– Impact: Established vendors will need formalized governance structures—small businesses may struggle without standardized toolkits; consultancies may prosper.
2. Impact Assessments (§1552.3, lines 3–15)
– “Deployers or third-party auditors shall complete an impact assessment … at deployment, annually, and post-substantial modification. Must include purpose; foreseeable discrimination risks; inputs/outputs; performance metrics; transparency and post-deployment monitoring.”
– Impact: Creates a recurring audit burden. Could discourage use of off‐the‐shelf AI unless vendor supplies assessment, stimulating a market for compliance services.
3. Consumer Notice & Adverse Decision Right to Appeal (§1552.5, lines 24–39)
– “Before deploying a high-risk AI system for a consequential decision … (i) notify consumer … (ii) disclose purpose; nature of decision; contact info; plain-language description; appeal instructions. If adverse, disclose principal reasons, data types, sources, and allow data correction or human review.”
– Impact: Reinforces “right to explanation.” May force financial institutions and insurers to slow automated underwriting or claims denials, integrate human‐in‐the‐loop.
4. Required Disclosure of AI Presence (§1554.1, lines 35–40)
– “Any person … offering AI decision systems intending to interact with consumers shall ensure … consumers … are interacting with an AI decision system.”
– Impact: Broad “AI labeling” mandate—public facing chatbots, recommendation engines must self-identify, increasing transparency but possibly reducing user engagement.
Section D: Enforcement & Penalties
1. Attorney General’s Exclusive Authority (§1556.1, lines 29–31)
– “The attorney general shall have exclusive authority to enforce the provisions of this article.”
2. Cure Period & Rebuttable Presumptions (§1556.2–3, lines 31–44)
– 2027–2028: AG must issue Notice of Violation allowing 60 days to cure. Post-2028 the AG may decide based on factors (number of violations; size/complexity; likelihood of harm).
– Deployment of red-teaming (§1556.6(a)(i–ii), lines 57–65) provides an affirmative defense if violations discovered by red-teaming are remediated within 60 days and aligned with NIST/ISO standards.
3. Unfair Trade Practice (§1556.5, lines 47–53)
– Violations constitute an “unfair trade practice” under GBL §349, enforceable only by AG, without private right of action.
4. Preemption & Exemptions (§1555, lines 48–83)
– Exempts AI systems regulated or approved by federal agencies (FDA, FAA, FHFA supervision) and HIPAA-covered health recommendations when not “high-risk” (§1555.4(d), lines 39–47).
Section E: Overall Implications
Positive Advances
– By codifying AI hallmarks (impact assessments, “model cards,” risk frameworks), New York could become a center for compliance‐tools innovation and set a de-facto standard for responsible AI.
– The red-teaming safe harbor and R&D carve-out protect early exploratory work.
Potential Restrictions
– Smaller startups may struggle to shoulder continuous auditing, documentation, and annual risk management reviews—favoring well-capitalized incumbents or specialized compliance firms.
– Broad “high-risk” scope (covering housing, education, legal services) could chill AI deployment in public‐sector procurement.
Ambiguities & Interpretations
– “Substantial factor” (§1550.14, lines 10–18) is undefined beyond “capable of altering outcome,” leaving room for litigation over whether a “recommendation” in a GUI qualifies.
– “Reasonable care” (§1551.1(a), lines 38–46) is open‐ended; full meaning hinges on future AG guidance and the identified “independent third parties.”
In sum, the bill embeds rigorous transparency and anti-discrimination safeguards for AI systems at every stage—from foundation model dev through consumer-facing deployment—while providing structured exemptions and cure processes. Its enforcement architecture, led solely by the Attorney General, aims to shape a regulatory ecosystem that prizes auditability and harm mitigation, but may raise barriers for smaller actors and ambiguous compliance costs.
Senate - 2487 - Enacts the New York artificial intelligence ethics commission act
Legislation ID: 68482
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. “Artificial intelligence” or “AI”
• Citation: “(a) ‘Artificial intelligence’ or ‘AI’ shall mean a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments.” (§ 106-c.2(a))
• Analysis: This explicit definition anchors the bill to software and systems that use algorithmic decision-making. It covers both predictive models and decision engines, whether deployed in physical devices or virtual services.
2. “Use”
• Citation: “(b) ‘Use’ shall mean the development, deployment, and operation of an AI system.” (§ 106-c.2(b))
• Analysis: By defining “use” broadly to include development, the statute encompasses research activities, testing, and live production systems.
3. Scope of Commission Oversight
• Citation: “5. The commission has the authority to oversee any AI system utilized by: (a) state agencies, for internal operations or public services; and (b) private companies operating in New York state, insofar as their AI usage impacts New York residents.” (§ 106-c.5)
• Analysis: The commission’s jurisdiction extends both to government use (5(a)) and to private sector AI that affects state residents (5(b)). This dual scope makes it a statewide regulatory regime for any impactful AI.
Section B: Development & Research
1. Ethical Guidelines & Reviews
• Citation: “4(a) establish ethical guidelines and standards for the development and deployment of AI technologies; (b) conduct reviews of AI projects for compliance with ethical standards.” (§ 106-c.4(a–b))
• Potential Impact: Researchers and startups will need to align R&D practices with state-mandated ethics guidelines and may face pre-deployment reviews. This could slow initial development but create clearer guardrails for responsible AI.
2. Research Requiring Informed Consent
• Citation: “6(g) conduct AI research that is harmful or without the informed consent of the subjects;” (§ 106-c.6(g))
• Analysis: Defines unethical research. Any data-collection or experimentation involving humans must include informed consent. Ambiguity remains around what constitutes “harmful” research, potentially requiring future rulemaking by the commission.
3. Educational Resources
• Citation: “4(c) provide educational resources for New York state agencies and the public on AI ethics;” (§ 106-c.4(c))
• Potential Impact: Could spur collaborative workshops and training programs, benefiting academic institutions and promoting best practices among developers.
Section C: Deployment & Compliance
1. Certification & Audits
• Citation: “4(f) develop certification for ethical AI systems and conduct periodic audits.” (§ 106-c.4(f))
• Analysis: A mandatory certification regime adds a compliance layer for vendors. Startups and established companies must prepare documentation and undergo periodic reviews to maintain certification.
2. Prohibited Practices
• Discrimination
– Citation: “6(a) utilize AI systems that systematically and unfairly discriminate against individuals or groups based on race, gender, sexuality, disability, or any other protected characteristic;” (§ 106-c.6(a))
• Misinformation
– Citation: “6(b) create or disseminate false or misleading information by an AI system to deceive users or the public;” (§ 106-c.6(b))
• Unauthorized Surveillance & Data Processing
– Citation: “6(c) use an AI system to unlawfully surveil, record, or disseminate information about an individual without their consent;” (§ 106-c.6(c)); “(d) participate in the unauthorized collection, processing, or dissemination of personal information by an AI system without their consent shall be deemed an infringement of privacy;” (§ 106-c.6(d))
• IP Infringement
– Citation: “6(e) participate in unauthorized use or reproduction of intellectual property through AI algorithms;” (§ 106-c.6(e))
• System Integrity Attacks
– Citation: “6(h) intentionally disrupt, damage, or subversion of an AI system to undermine its integrity or performance;” (§ 106-c.6(h))
• Identity Fraud
– Citation: “6(i) participate in the unauthorized use of someone’s personal identity or data by artificial intelligence systems to commit fraud or theft.” (§ 106-c.6(i))
• Analysis: These clauses explicitly shape permissible AI behavior, effectively creating a code of conduct. Companies must audit models for bias, implement user-consent flows, secure data, and license IP appropriately.
Section D: Enforcement & Penalties
1. Civil and Criminal Penalties
• Citation: “7. The commission shall have the power to impose penalties, including fines and injunctions for violations…” (§ 106-c.7)
• Non-Economic Harm
– “(a) Where harm is non-economic, a civil penalty, including injunctions and other damages.” (§ 106-c.7(a))
• Economic Harm or Systematic Privacy Breach
– “(b) Where economic harm is established or there is a systematic breach of privacy, criminal offenses shall be prosecuted by the attorney general.” (§ 106-c.7(b))
• Analysis: Tiered enforcement means that reputational, injunctive relief is possible for ethics violations, while large-scale data breaches or financial losses can trigger criminal prosecution. Regulators gain broad discretion.
2. Complaint Investigations
• Citation: “4(e) receive and investigate complaints regarding unethical AI practices;” (§ 106-c.4(e))
• Impact: Any individual or organization can trigger an audit or investigation. This may increase litigation risk and necessitate dedicated compliance units within companies.
Section E: Overall Implications
1. Regulatory Certainty vs. Compliance Burden
• The commission’s guidelines (§ 106-c.4(a)) and certification (§ 106-c.4(f)) offer a clear standard for ethical AI, potentially benefiting developers seeking safe harbor. However, the administrative cost of regular audits and reviews will weigh more heavily on smaller teams.
2. Research Environment
• Informed-consent requirements (§ 106-c.6(g)) and pre-deployment reviews (§ 106-c.4(b)) could slow experimental AI research but may enhance public trust, leading to higher adoption of state-endorsed projects.
3. Private Sector Impact
• Broad scope of oversight (§ 106-c.5(b)) signals that any AI touching New Yorkers triggers compliance. National and global AI vendors must adjust privacy, anti-bias, and transparency practices specifically for New York users.
4. Enforcement Deterrence
• Criminal liability for economic harm or privacy breaches (§ 106-c.7(b)) raises stakes significantly. Companies may invest more in robust security and bias-mitigation tooling to avoid AG action.
5. Policy Leadership
• Annual reports (§ 106-c.8) position New York as an AI policy thought leader, potentially influencing other states or federal rulemaking through shared best practices and documented outcomes.
Senate - 4276 - Enacts the "digital fairness act"
Legislation ID: 70060
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. “Automated decision system” (ADS) (State Finance Law § 165-10.a.(i) and § 165-11.a.(i))
– “Any software, system, or process that is designed to aid or replace human decision making. Such term may include analyzing complex datasets to generate scores, predictions, classifications, or some recommended action or actions, which are used by agencies to make decisions that impact human welfare.”
– Relevance: This is a textbook definition of an AI-driven system, explicitly covering machine learning and algorithmic decision-support.
2. “Training data” (SFL § 165-10.a.(ii))
– “The datasets used to train an automated decision system, machine learning algorithm, or classifier to create and derive patterns from a prediction model.”
– Relevance: Targets core AI development artifacts, identifying them for assessment.
3. “Automated decision system impact assessment” (SFL § 165-10.a.(ii))
– A “study evaluating an automated decision system and the automated decision system’s development processes, including … design and training data … for statistical impacts on classes protected … as well as for impacts on privacy and security …”
– Relevance: Imposes an AI-specific audit at procurement time.
4. “Automated decision system use policy” (SFL § 165-11.a.(ii))
– A public document describing capabilities, data sources, security safeguards, audit mechanisms, deployment rules, human-review processes, etc.
– Relevance: Establishes a transparency requirement tailored to algorithmic systems.
Section B: Development & Research
No explicit funding mandates or data-sharing rules for private AI research appear in the digital fairness act or the executive law amendments. However:
– SFL § 165-10.b.(ii) requires completion of impact assessments for “existing automated decision system[s]” within one year and “new … prior to acquisition.” This indirectly impacts research by governing when agencies may test or deploy internally developed AI.
– SFL § 165-10.f directs the Office of Information Technology Services (OITS), in consultation, to “complete and publish … a comprehensive study of the statistical impacts of automated decision systems on classes protected” within two years. This may influence what data researchers can access when partnering with government.
Section C: Deployment & Compliance
1. Third-party impact assessments (SFL § 165-10.b)
– “The state … shall not … procure, acquire, employ, use, deploy, or access information from an automated decision system unless it first engages a neutral third party to conduct an automated decision system impact assessment …” (§ 165-10.b)
– Impact: Vendors must submit to external audits before sale. Increases compliance costs for startups/SMEs.
2. Public comment on assessments (SFL § 165-10.c)
– “Upon publication of an automated decision system impact assessment, the public shall have forty-five days to submit comments … The state … shall consider such public comments … and shall post responses.”
– Impact: Slows procurement timelines; invites community scrutiny.
3. Open-source requirement (State Finance Law § 8.21)
– “No payment shall be made for an automated decision system … unless the automated decision system uses only open source software … and the acquiring agency has complied with the … requirements in section 165.”
– Impact: Excludes proprietary AI offerings from government use; encourages open-source AI adoption.
4. Legislative approval & public hearing (SFL § 165-11.c)
– Before acquiring or using any ADS that “assigns or contributes to the determination of rights, benefits, opportunities, or services for an individual,” agencies must secure council or legislature approval after a “properly-noticed, germane, public hearing.”
– Impact: Drastically raises barriers to deploying AI for social services or benefits.
5. ADS use policy publication (SFL § 165-11.b)
– Must publish an ADS use policy “at least ninety days … prior to the … acquisition or deployment” and “within one hundred eighty days” for existing systems.
– Impact: Creates transparency obligations for any agency-sponsored AI.
Section D: Enforcement & Penalties
The bill does not attach fine schedules specifically to the ADS provisions beyond general administrative oversight. Penalties are:
– If agencies proceed without assessment or policy, procurement is invalid since “no payment shall be made” (§ 8.21, § 8.22, § 8.23, § 8.24, § 8.25). In effect, vendors will not get paid.
– Non-compliance with public-comment and hearing requirements could be challenged administratively or in court as an ultra vires act.
Section E: Overall Implications
1. Transparency & Accountability: By mandating impact assessments, public comment, and use-policy publication, the state imposes a rigorous review process on any AI system that influences rights or benefits.
2. Barrier to Entry for Private AI Vendors: The open-source requirement (§ 8.21) and mandatory third-party audits raise costs, disadvantaging small startups with proprietary models.
3. Research Unaffected, Government-Led AI Restrained: There is no direct funding for academia or data sharing, but government adoption of AI will slow down; agencies must navigate legislative hearings.
4. Civil-Rights Focus: The emphasis on “statistical impacts on classes protected under section 296 of the Executive Law” (§ 165-10.a.(ii)(C)) positions AI oversight as a civil-rights measure, potentially inspiring similar private-sector compliance demands.
5. Potential Chilling Effect: Extended timelines (90–180 days), hearings, and “no payment” penalties may discourage innovation in public AI deployment, but could set a high-standard template for ethically robust AI.
Ambiguities:
– “Neutral third party” is undefined beyond qualification rules to be set by the State Procurement Council (§ 165-10.d). Whether for-profit auditors qualify is unclear.
– The scope of “rights, benefits, opportunities, or services” requiring legislative approval (§ 165-11.c) may be interpreted narrowly (e.g., only welfare decisions) or broadly (any agency decision), potentially encompassing numerous minor decisions.
Senate - 4394 - Establishes criteria for the sale of automated employment decision tools
Legislation ID: 70178
Bill URL: View Bill
Sponsors
Senate - 5486 - Relates to the use of telematics systems by automobile insurers
Legislation ID: 71270
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an analysis organized in five prescribed sections. All citations give the bill’s section, subsection or line numbers exactly as in the printed text.
Section A: Definitions & Scope
1. “Telematics system” as an AI‐relevant definition
• Citation: “(u) For the purposes of this article a telematics system shall mean technology which monitors, stores and transmits information such as motor vehicle location, driver behavior, engine performance and motor vehicle activity.” (New § 2313(u), lines 19–23)
– Analysis: The bill explicitly defines “telematics system” to include real-time data collection and transmission. Modern telematics systems typically employ machine‐learning algorithms to infer driver risk, optimize routing or detect anomalies. By requiring vendors to file their “model or algorithm” (New § 2313(u), line 23), the bill implicitly treats these algorithms as AI systems subject to regulatory scrutiny.
2. “Third-party developers or vendors”
• Citation: “It shall also include any third-party developers or vendors of telematics systems as such term is defined in this section.” (Amended § 2313(a), lines 10–12)
– Analysis: This broad definition brings in not only traditional insurers but also data suppliers and AI solution providers who create or embed predictive models. This ensures that AI-powered products used for insurance rating are covered.
Section B: Development & Research
The bill contains no direct mandates for AI research funding, reporting of experimental results, or data‐sharing for R&D. It focuses entirely on commercial use of telematics (i.e., AI) in underwriting and rating.
Section C: Deployment & Compliance
1. Model filing and risk linkage
• Citation: “provide to the superintendent an explanation of how the factors used in the model or algorithm are connected to risk and demonstrate that each factor used is related to risk of loss and incorporated in a manner that directly reflects that relationship.” (New § 2304-a(a)(1), lines 5–8)
– Analysis: Insurers and vendors must disclose their AI scoring factors and justify each factor’s causal or statistical link to risk. This transparency requirement could slow deployment by imposing regulatory review of model logic.
2. Public disclosure of scoring methodologies
• Citation: “publicly disclose scoring methodologies.” (New § 2304-a(a)(2), line 9)
– Analysis: Vendors must make their AI/ML scoring approaches publicly available. This could spur competition among startups by lowering information asymmetry, but may also force proprietary models into open view, chilling investment if intellectual property cannot be protected.
3. Bias testing and disparate impact reporting
• Citation: “report to the superintendent on what testing was done to ensure that the telematics system does not result in discrimination against any protected classes or to reduce disparate impact on such classes” (New § 2304-a(a)(3), lines 10–13)
– Analysis: Requires algorithmic fairness audits akin to bias testing in AI. Startups and researchers will need to invest in fairness tools and methodologies. Regulators gain visibility into potential harms before deployment.
4. Consumer data access rights
• Citation: “allow consumers to request access to the data collected by a telematics system … and provide the data in a readable format.” (New § 2304-a(a)(4), lines 14–16)
– Analysis: Aligns with data-portability principles in AI regulation. End‐users (drivers) can see raw sensor readings or derived risk scores, which may aid in contesting errors but requires vendors to build user‐facing interfaces.
5. Purpose limitation on data use
• Citation: “No insurer or third-party developer or vendor of telematics systems shall use any data collected for any purpose other than underwriting and rating decisions.” (New § 2304-a(b), lines 17–20)
– Analysis: Restricts cross-selling or marketing uses of driver data, limiting business models of AI analytics firms that aggregate telematics and consumer data for ad targeting or other purposes.
6. Anti-discrimination on external data and models
• Citation:
(1) “shall not unfairly discriminate based on race, color, …” (New § 2304-a(c)(1), lines 21–24)
(2) “nor use any algorithms or predictive models that use external consumer data and information sources, in a way that unfairly discriminates …” (New § 2304-a(c)(2), lines 25–29)
– Analysis: This goes beyond pure telematics‐based factors to any “external consumer data” or third-party AI models. Insurers must ensure fairness not only in their own algorithms but also in any off‐the‐shelf AI services they integrate. This places compliance burdens on vetting third‐party code.
Section D: Enforcement & Penalties
1. Superintendent rule‐making authority
• Citation: “the superintendent is hereby authorized and empowered to promulgate such rules and regulations as may … be appropriate for the effective administration of this article.” (New § 2304-a(d), lines 31–34)
– Analysis: Gives the insurance regulator broad discretion to flesh out testing protocols, documentation standards, audit frequencies, and potentially penalties. Vendors and insurers must monitor future guidance or risk noncompliance.
2. Implied supervisory enforcement
• No explicit civil or criminal penalties are listed in § 2304-a. However, under existing insurance‐law powers (e.g., § 2304), the superintendent can impose fines, cease‐and‐desist orders or license suspensions for unfair trade practices, which would presumably cover violations of these new requirements.
Section E: Overall Implications
1. Transparency and Accountability: By mandating model filing, public disclosure, and bias testing, the bill shifts New York’s auto‐insurance telematics market toward greater algorithmic transparency. This is likely to benefit consumer advocates and privacy‐conscious drivers but may raise costs for smaller AI vendors.
2. Compliance Costs vs. Innovation: Startups developing novel driver‐risk AI systems will face new compliance overhead—documenting factor linkages, auditing for bias, building consumer data portals—potentially favoring larger incumbents who can amortize those costs.
3. Narrowing Use‐Cases: Purpose limitations (only underwriting/rating) and anti‐marketing clauses constrain ancillary AI data‐monetization models (e.g., selling driving data for location‐based advertising).
4. Regulatory Precedent: This bill could serve as a template for AI regulation in other insurance lines (property, health) or other states, embedding the notion that “algorithmic fairness” and “consumer access” are statutory requirements for any AI embedded in regulated financial services.
5. Ambiguities to Watch:
– “Unfairly discriminate” and “disparate impact” are not formally defined, leaving open questions about thresholds for violation.
– The scope of “external consumer data” in § 2304-a(c)(2) could sweep in large public datasets or social-media signals, depending on future rule‐making.
– No standard formats for “readable” consumer data—this may lead to disputes over compliance.
In sum, while framed as an insurance‐law amendment focused on telematics, this bill is effectively a first‐generation AI transparency and fairness regime for any AI system used in personal auto underwriting in New York.
Senate - 5668 - Relates to liability for misleading, incorrect, contradictory or harmful information provided to a user by a chatbot
Legislation ID: 71452
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. “Artificial intelligence” (sec. 1, subd. (1)(a))
– Text: “Artificial intelligence means a machine-based system or combination of systems…that for explicit and implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions…”
– Analysis: This definition explicitly targets AI by focusing on systems that learn from inputs to produce outputs. It would cover large language models, recommendation engines, image generators, etc.
2. “Chatbot” (sec. 1, subd. (1)(b))
– Text: “Chatbot means an artificial intelligence system…that simulates human-like conversation and interaction…”
– Analysis: By defining “chatbot” as an AI application simulating human dialogue, the bill clearly scopes its liability rules to conversational AI agents.
3. “Companion chatbot” (sec. 1, subd. (1)(c))
– Text: “Companion chatbot means a chatbot that is designed to provide human-like interaction that simulates an interpersonal relationship…”
– Analysis: Introduces a subclass of chatbot that learns from past interactions and mimics roles (romantic, therapeutic, etc.). These are targeted due to their deeper user engagement and attendant risks (e.g. self-harm encouragement).
4. “Proprietor” (sec. 1, subd. (1)(g))
– Text: “Proprietor means…any person, business…that owns, operates or deploys a chatbot used to interact with users.”
– Analysis: Holds developers and deployers of AI chatbots—rather than mere licensors—directly liable under the bill.
Section B: Development & Research
– There are no direct R&D funding mandates, reporting requirements, or data-sharing rules for AI developers or academic researchers. The bill is focused on post-deployment consumer protections and liability, not on incentivizing or guiding research.
Section C: Deployment & Compliance
1. Liability for False or Harmful Information (sec. 2)
a. Civil liability if “chatbot provides materially misleading, incorrect, contradictory or harmful information…that results in financial loss or other demonstrable harm” (sec. 2(a)).
b. No disclaimer permitted merely by notifying users they are talking to a non-human (sec. 2(c)).
– Impact: Vendors must implement robust content-validation pipelines, insurance reserves, and quick remediation processes to correct misinformation within 30 days or risk suit. Start-ups may face higher compliance costs; established vendors will need extensive logging and correction workflows.
2. Bodily Harm and Self-Harm Liability (sec. 3)
– Text: “A proprietor…may not disclaim liability…where a chatbot provides materially misleading…information…that results in bodily harm…including any form of self-harm.”
– Impact: Raises stakes for companion chatbots especially. Providers must integrate real-time safety checks and possibly human escalation for sensitive queries.
3. Mandatory Disclosure (sec. 4)
– Text: “Shall provide clear, conspicuous and explicit notice…that they are interacting with an artificial intelligence chatbot…no smaller than the largest font size of other text…”
– Impact: Uniform labeling requirement. All interfaces must include AI-vs-human disclaimers in plain sight, affecting UI/UX design and potentially user trust.
Section D: Special Protections for Self-Harm and Minors
1. Self-Harm Safeguards (sec. 5(a))
– Text: “Proprietor…shall use commercially reasonable and technically feasible methods to…prevent such companion chatbot from promoting, causing or aiding self-harm…and determine whether a covered user is expressing thoughts of self-harm…and prohibit continued use for 24 hours…and prominently display a means to contact a suicide crisis organization.”
– Liability triggers for non-compliance if self-harm occurs (sec. 5(b)-(c)).
– Impact: AI vendors must integrate sentiment analysis, trigger word detection, and referral flows to crisis hotlines. Regulators will later define “commercially reasonable,” but ambiguity remains as to acceptable false-positive and false-negative rates.
2. Minor Protections (sec. 6)
– Age-verification requirement: “Shall use commercially reasonable and technically feasible methods to determine whether a covered user is a minor” (sec. 6(a)).
– If minor: “Cease use until…verifiable parental consent” and block for three days if self-harm content emerges (sec. 6(b)). Strict liability if a minor self-harms after non-compliance (sec. 6(c)).
– Impact: Will push vendors toward identity verification services, digital KYC, or app-store parental-control integrations. The costs and privacy trade-offs for collecting age data could be significant, especially for small developers. Ambiguity over “verifiable parental consent” methods will require forthcoming AG regulations.
Section E: Enforcement & Penalties
1. Attorney General Rule-making (sec. 8, 10, 11)
– The Attorney General must define “commercially reasonable and technically feasible methods” for self-harm prevention, age checks, and parental consent.
– Text (sec. 8(a)): “The attorney general shall promulgate regulations identifying commercially reasonable and technically feasible methods…”
– Impact: The AG’s upcoming regulations will determine compliance costs and technical standards. The bill leaves substantial discretion—and potential uncertainty—to the regulator.
2. Safe-Harbor for Correction (sec. 2(a))
– Text: “No liability…where the proprietor has corrected … and substantially or completely cured the harm…within thirty days of notice.”
– Impact: Provides a remediation window but requires tracking and notice mechanisms for harmed users.
3. Strict Liability for Minors (sec. 6(c)) and Non-waivable Liability (sec. 5(d), 6(d))
– Text: “A proprietor…may not waive or disclaim liability…”
– Impact: No contractual disclaimers or terms of service can sidestep these duties, limiting vendor flexibility.
Overall Implications
– Advance Consumer Safety: The bill zeroes in on AI chatbots’ propensity to mislead or harm, especially vulnerable populations (minors, self-harming users).
– Restrict and Increase Costs: Imposes technical and compliance burdens on all chatbot deployers—likely favoring larger vendors able to absorb age checks, sentiment analysis, and legal teams.
– Ambiguities & Regulatory Discretion: Key terms (“commercially reasonable,” “technically feasible,” “verifiable parental consent”) are undefined, shifting the real power to the AG’s forthcoming regulations.
– Industry Response: Start-ups may pivot away from companion chatbot features to avoid strict liability, or geo-filter New York users. Established vendors may lobby for clearer standards or try to standardize best practices nationally.
Senate - 6278 - Establishes the crime of aggravated harassment by means of electronic or digital communication and provides for a private right of action for the unlawful dissemination or publication of deep fakes
Legislation ID: 72062
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused read of S.6278 (2025–26), organized per your requested outline. All quotations cite the bill’s section, subdivision, and line ranges.
Section A: Definitions & Scope
1. “Deep fake” (new Penal Law § 240.80(2), pp. 1–2)
– Text: “For purposes of this section, ‘deep fake’ means a digitized image that is altered to incorporate a persons face or their identifiable body part onto such image ….”
– Comment: Although styled as a harassment statute, this definition explicitly targets images “digitized”—i.e. created or manipulated by software, AI, machine learning, or other “computer-generated or technological means.” It thus plainly sweeps in AI-generated or AI-altered content.
2. “Digitization” (Civil Rights Law § 52-b(6-a)(a)–(b), p. 3, lines 10–23)
– Text: “For purposes of this section, … ‘digitization’ means the use of software, machine learning, artificial intelligence, or any other computer-generated or technological means, including adapting, modifying, manipulating, or altering a realistic depiction.”
– Comment: This expansive definition covers any AI-based image synthesis or editing.
3. Parallel Definitions in § 52-c (p. 4, lines 54–5):
– “Digitization” and “deep fake” are re-stated in the private-cause-of-action for sexually explicit deep fakes.
Section B: Development & Research
– No provisions in this bill address AI R&D funding, reporting requirements, data-sharing, or grants. The focus is entirely on civil and criminal liability for AI-altered images, not on promoting or overseeing AI research.
Section C: Deployment & Compliance
1. Criminal Prohibition of AI-altered harassment (Penal Law § 240.80)
– Text (§ 240.80(1), p. 1, lines 5–10): “…with the intent to harass, annoy, threaten or alarm another person, such person produces, distributes, publishes or broadcasts material that contains … a deep fake into which the image of another person … is superimposed.”
– Impact: Any individual or platform distributing AI-generated nonconsensual deep fakes could face a class A misdemeanor. Startups or vendors offering AI-image-manipulation services must implement user-controls, moderation, or terms of service to avoid aiding such conduct.
2. Civil Removal & Damages (Civil Rights Law §§ 52-b, 52-c)
– Private Right to Sue (§ 52-b(1)(a), p. 2, lines 14–19): “…a cause of action…where such image or deep fake … was disseminated or published … without the consent of such person.”
– Website Jurisdiction (§ 52-b(5)(a)–(b), p. 3, lines 47–4): “Any website that hosts or transmits … viewable in this state … without the consent … shall be subject to personal jurisdiction in a civil action in this state….”
– Impact: Online platforms—even those based elsewhere—that allow users to post AI-generated intimate or violent deep fakes risk being hauled into New York courts. This creates strong incentives for content moderation, takedown procedures, and possibly proactive filters for AI‐altered content.
Section D: Enforcement & Penalties
1. Criminal Penalty for Aggravated Harassment (§ 240.80, p. 1, lines 19–21):
– “Aggravated harassment by means of electronic or digital communication shall be a class A misdemeanor.”
2. Civil Remedies (§ 52-b(2), p. 2, lines 21–24; § 52-c(5), p. 5, lines 7–10):
– “The finder of fact…may award injunctive relief, punitive damages, compensatory damages and reasonable court costs and attorneys fees.”
3. Statute of Limitations (§ 52-b(6), p. 3, lines 5–9; § 52-c(6), p. 5, lines 11–16):
– “Commenced the later of … three years after dissemination … or one year from discovery.”
Section E: Overall Implications
– The bill does not regulate AI research or mandate AI governance frameworks. Rather, it employs AI-centric definitions (“deep fake,” “digitization”) to extend existing harassment and privacy torts to AI-generated or AI-modified images.
– By criminalizing nonconsensual AI-altered imagery and empowering civil suits against both distributors and hosting platforms, the state raises the compliance bar for:
• AI start-ups offering image-synthesis tools (must implement abuse prevention).
• Established platforms (face expanded jurisdiction and liability).
• Researchers (if they publish demonstration deep fakes without permission).
– Ambiguity remains as to “for no other legitimate purpose” (Penal Law § 240.80(2), p. 1, lines 15–19), which could be read narrowly—targeting only harassment—or broadly to outlaw benign AI-enabled manipulations unless specifically authorized. This may chill innovation absent clear guidance.
Senate - 6301 - Creates a temporary state commission to study and investigate how to regulate artificial intelligence, robotics and automation
Legislation ID: 72085
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an analysis of S.6301 (2025-2026), a bill establishing a temporary state commission “to study and investigate how to regulate artificial intelligence, robotics and automation.” Every observation is anchored to the bill’s text.
Section A: Definitions & Scope
1. No explicit statutory definitions. The bill never defines “artificial intelligence,” “robotics,” or “automation” in technical or legal terms. This omission leaves considerable ambiguity about what technologies will fall under the commission’s review.
• Citation: “A temporary state commission… to study and make determinations on issues including but not limited to: (a) current law within this state addressing artificial intelligence, robotics and automation…” (Section 1, lines 1–6)
2. Broad scope statement. By listing AI, robotics, and automation together and adding “including but not limited to,” the bill implicitly contemplates any software- or hardware-driven system capable of autonomous or semi-autonomous action.
• Citation: Section 1, lines 1–5
Section B: Development & Research
The commission’s mandate is purely investigatory; it does not itself allocate funding or impose research mandates. However, several subsections in Section 1 could shape future R&D policy recommendations:
1. Comparative policy analysis (Section 1(b), lines 7–9). The commission must “study … comparative state policies” that have “aided in creating a regulatory structure for AI, robotics and automation,” which may lead to recommendations on data-sharing rules or R&D coordination.
2. Public‐sector applications (Section 1(h), lines 20–21). The impact of AI in government operations (e.g. welfare-eligibility algorithms, predictive policing) is explicitly on the agenda. Its findings could create pressure for state research collaborations or procurement guidelines.
Potential impact on R&D:
• Researchers may anticipate new reporting requirements if the commission advocates for them.
• Startups could see seed regulatory frameworks suggested, influencing investor confidence.
Section C: Deployment & Compliance
While the bill does not itself impose regulations, it directs the commission to analyze aspects that would directly affect commercial deployment:
1. Liability framework (Section 1(c), lines 10–12). “Criminal and civil liability regarding violations of law caused by entities equipped with AI, robotics and automation” is an explicit subject. Recommendations could propose new liability rules or safe-harbor provisions for developers.
2. Confidential information (Section 1(e), lines 13–15). The commission must study “the impact of AI … on the acquiring and disclosure of confidential information,” potentially leading to future data-privacy regulations or vendor certification schemes.
3. Weaponization (Section 1(f), lines 16–17). Study of “potential restrictions on the use of AI … in weaponry” could result in export-control style rules that commercial defense contractors must comply with.
Implications for deployment:
• Established vendors may lobby to shape liability standards in their favor.
• End-users (businesses, consumers) could face new compliance costs once the commission’s recommendations are enacted.
Section D: Enforcement & Penalties
This bill contains no enforcement provisions, penalties, or incentives. Its sole regulatory power is the creation of a fact‐finding body.
• No certification or auditing authority is granted.
• No fines or criminal penalties are established.
• No grant or incentive program is authorized.
• Citation: The act “shall expire and be deemed repealed December 31, 2026” (Section 6, lines 24–25), emphasizing its temporary, investigatory nature.
Section E: Overall Implications
1. Foundation for future AI law. By convening stakeholders (governor, legislative leaders, SUNY/CUNY chancellors, AG), the commission is likely to produce a report that becomes the blueprint for permanent regulation.
2. Industry alert. Startups, universities and vendors should monitor the commission’s hearings (Section 4, lines 15–18) and prepare testimony.
3. Regulatory uncertainty persists. Absence of definitions and enforcement rules means that until repeal or successor legislation materializes, no immediate compliance steps are required—but the landscape could change rapidly after the report.
4. Ambiguities. Terms like “automation” and “robotics” are left undefined; they could be interpreted narrowly (industrial robots) or broadly (RPA software). The breadth of “including but not limited to” suggests the commission could extend its study to emerging areas such as generative AI, autonomous vehicles, or decision-support systems.
In summary, S.6301 does not itself regulate AI, but it authorizes a high-level review that will directly inform future mandates on liability, data privacy, weapons restrictions and public-sector adoption. Stakeholders should engage now to influence the commission’s final report.
Senate - 6471 - Relates to the use of automated decision tools by landlords for making housing decisions
Legislation ID: 99614
Bill URL: View Bill
Sponsors
Senate - 6748 - Requires publications to identify when the use of artificial intelligence is present within such publication
Legislation ID: 99887
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a structured analysis of Senate Bill 6748 (2025-2026 Regular Session), as introduced by Senator Webb, which would add a new § 338 to New York’s General Business Law. I have organized the response into five sections (A–E), with direct quotations from the bill to support each point.
—
Section A: Definitions & Scope
1. “Generative artificial intelligence” definition (lines 3–22)
• “For purposes of this section, ‘generative artificial intelligence’ shall mean the use of machine learning technology, software, automation, and algorithms to perform tasks or to make rules and/or predictions based on existing data sets and instructions, including, but not limited to:” (lines 3–7)
– This opening phrase explicitly signals that the new law is targeting AI systems that generate or transform content.
• Sub-clauses (a) through (e) (lines 8–22) enumerate illustrative categories of covered systems:
a) “any artificial system that performs tasks under varying and unpredictable circumstances without significant human oversight, or that can learn from experience and improve performance when exposed to data sets;” (lines 8–11)
b) “an artificial system developed in computer software, physical hardware, or other context that solves tasks requiring human-like perception, cognition, planning, learning, communication, or physical action;” (lines 11–14)
c) “an artificial system designed to think or act like a human, including cognitive architectures and neural networks;” (lines 15–16)
d) “a set of techniques, including machine learning, that is designed to approximate a cognitive task;” (lines 17–18)
e) “an artificial system designed to act rationally, including an intelligent software agent or embodied robot that achieves goals using perception, planning, reasoning, learning, communicating, decision making, and acting.” (lines 19–22)
• Analysis of scope:
– Very broad: Covers software, hardware, embodied robots, agents, and any method “including, but not limited to” machine learning.
– Ambiguity: “Other information communication technology” (line 2) could encompass simple automation tools; courts or regulators might need to interpret where the line is drawn.
Section B: Development & Research
• The bill contains no provisions that directly fund, mandate reporting by, or otherwise shape research practices.
• It does not impose data-sharing requirements or grant R&D incentives.
• Potential indirect effects:
– Researchers developing generative models may have to label any outputs published in journals or online if they meet the broad definition (lines 3–22).
– Ambiguity in what counts as “publication” might chill academic blogs or preprint servers unless clarified.
Section C: Deployment & Compliance
1. Labeling requirement (lines 23–7)
• “Every newspaper, magazine or other publication printed or electronically published in this state, which contains an article, periodical, photograph, video or other visual image which was wholly or partially composed or authored through the use of generative artificial intelligence or other information communication technology, shall conspicuously imprint on the top of the page or webpage of such publication that such article, periodical, photograph, video or other visual image was composed through the use of artificial intelligence or other information communication technology.” (lines 23–7)
• Parties affected:
– Traditional media (newspapers, magazines) and “other publications” (which could include blogs, newsletters, social-media posts).
• Compliance considerations:
– “Conspicuously imprint on the top of the page or webpage” sets a design/location standard but leaves format and font size unspecified.
– Publishers must track provenance of every visual and written piece to determine if an AI tool participated—operationally burdensome, especially for aggregated or syndicated content.
• Potential reshaping:
– Encourages transparency about AI involvement in media.
– May slow or deter small publishers and independent writers who cannot easily audit every paragraph or image for AI involvement.
Section D: Enforcement & Penalties
• The text of the bill, as offered, contains no explicit enforcement mechanism (e.g., fines or injunctive relief).
• There is no mention of which agency or regulator would oversee compliance.
• Ambiguity:
– Without a penalty scheme, compliance may rely on private causes of action under New York’s existing consumer-protection statutes, but that is not spelled out.
– Regulators (e.g., the Attorney General’s Office) may need rule-making authority or further legislation to define penalties.
Section E: Overall Implications
• The bill’s core aim is transparency: it requires labeling of AI-generated or AI-assisted content in printed or electronic “publications.”
• Positive effects:
– Increases public awareness of AI’s role in media generation.
– May deter malicious use of deepfakes in news outlets.
• Negative or chilling effects:
– Broad definitions may sweep in mundane uses of automation (e.g., spell-check, headline-suggestion tools) under “information communication technology,” potentially over-burdening publishers.
– Lack of clarity about enforcement could result in uneven application or a patchwork of private lawsuits.
• On the state’s AI ecosystem:
– Startups providing AI-driven content-creation services will need to integrate labeling metadata into their APIs or delivery pipelines.
– Established vendors might adapt quickly, but smaller operators could struggle with the compliance burden.
– Researchers and academics who publish to online platforms must consider the labeling requirement when disseminating draft or final articles containing AI-assisted text or images.
In summary, S. 6748 explicitly targets “generative artificial intelligence” (lines 3–22) and imposes a conspicuous labeling requirement for AI-involved content in any New York publication (lines 23–7). It does not address research funding, data sharing, certification, or enforcement mechanisms, leaving significant gaps in implementation detail. The net effect would be to push publishers and platform operators toward disclosing AI usage while creating new compliance obligations—and potential uncertainty—around what tools and content fall under the law.
Senate - 6751 - Excludes a production using artificial intelligence or autonomous vehicles in a manner which results in the displacement of employees from the definition of qualified film
Legislation ID: 99894
Bill URL: View Bill
Sponsors
Senate - 6953 - Relates to the training and use of artificial intelligence frontier models
Legislation ID: 100092
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a structured analysis of New York S.6953 (the “RAISE Act”) focusing on its AI-related provisions. Citations are drawn directly from the bill text (section and subdivision numbers in brackets).
Section A: Definitions & Scope
1. “Artificial intelligence” and “Artificial intelligence model”
– “Artificial intelligence means a machine-based system that … make predictions, recommendations, or decisions influencing real or virtual environments…” [§1420(2), lines 22–27]
– “Artificial intelligence model means an information system or component of an information system that implements artificial intelligence technology…” [§1420(3), lines 28–33]
→ These capture the classic ML/AI workflow (perception, modeling, inference) and explicitly target any software component using statistical or machine-learning techniques.
2. “Frontier model”
– “Frontier model means … an artificial intelligence model trained using greater than 10^26 computational operations … the compute cost of which exceeds one hundred million dollars.” [§1420(6)(a), lines 18–24]
– It also covers models distilled from such large models [§1420(6)(b), lines 22–24].
→ By defining “frontier model” via compute size and cost thresholds, the bill zeroes in on state-of-the-art large language models or multimodal models (GPT‐scale and above).
3. “Compute cost”
– “Compute cost means the cost incurred to pay for compute used in training a model when calculated using the average market prices of cloud compute in the United States at the start of training such model as reasonably assessed by the person doing the training.” [§1420(4), lines 8–13]
→ Ties compliance obligations to a quantifiable and auditable metric.
4. “Safety and security protocol”
– Defined as “documented technical and organizational protocols that … appropriately reduce the risk of critical harm,” including test procedures and administrative controls [§1420(12), lines 10–33].
→ Explicitly requires large developers to formalize AI-specific risk-mitigation processes.
5. “Safety incident” and “Critical harm”
– “Safety incident means … demonstrable evidence of an increased risk of critical harm” such as unwanted autonomous behavior or theft of model weights [§1420(13), lines 34–43].
– “Critical harm” includes death or serious injury of 100+ people or ≥$1 billion in damages, including through AI-enabled weapons or autonomous criminal activity [§1420(7), lines 25–33].
→ Sets thresholds for when AI behavior or misuse rises to a reportable event.
Section B: Development & Research
1. Obligations before training a planned frontier model
– “Any person who is not a large developer, but who sets out to train a frontier model … shall, before training such model: (a) Implement a written safety and security protocol … (b) Conspicuously publish a copy of the safety and security protocol … and transmit … to the attorney general.” [§1421(9)(a)–(b), lines 28–41]
→ Forces even emerging teams to adopt AI safety documentation early.
2. Exemption for academic research
– “Accredited colleges and universities shall not be considered large developers … to the extent that such colleges and universities are engaging in academic research.” [§1420(9), lines 48–53]
→ Protects university-led AI research from the full scope of these rules.
Section C: Deployment & Compliance
1. Transparency requirements before deployment
– “Before deploying a frontier model … the large developer … shall: (a) Implement a written safety and security protocol; (b) Retain an unredacted copy … for as long as the frontier model is deployed plus five years; (c) Conspicuously publish a copy … with appropriate redactions and transmit a copy … to the attorney general; (d) Record … tests and test results …; (e) Implement appropriate safeguards to prevent unreasonable risk of critical harm.” [§1421(1)(a)–(e), lines 1–21]
→ Imposes a transparent, multi-year audit trail for model safety reviews.
2. Prohibition on unsafe deployments
– “A large developer shall not deploy a frontier model if doing so would create an unreasonable risk of critical harm.” [§1421(2), lines 22–24]
3. Annual reviews and third-party audits
– “A large developer shall conduct an annual review of any safety and security protocol … and, if necessary, make modifications … If any modifications are made, … republish … with redactions” [§1421(3), lines 25–30]
– “Beginning … a large developer shall annually retain a third party to perform an independent audit … report … (i) detailed assessment … (ii) any instances of noncompliance … (iii) assessment of internal controls … (iv) signature of lead auditor.” [§1421(4)(a)–(c), lines 31–53]
→ Establishes recurring external validation of AI practices.
4. Safety incident reporting
– “A large developer shall disclose each safety incident affecting the frontier model to the attorney general within seventy-two hours … including: (a) date … (b) reasons incident qualifies … (c) short and plain statement describing the incident.” [§1421(6), lines 9–17]
5. Conflict with federal contracts
– Exempts products/services where requirements “would strictly conflict with the terms of a contract with a federal government entity” [§1421(7)(a), lines 18–21] but otherwise applies broadly [§1421(7)(b), lines 22–24].
Section D: Enforcement & Penalties
1. Civil enforcement by Attorney General
– “The attorney general may bring a civil action … and recover: (a) For a violation of section 1421 … a civil penalty not exceeding 5% of the total compute cost … for a first violation and 15% … for any subsequent violation. (b) For a violation of section 1422 (employee whistleblower protections), a civil penalty up to $10,000 per employee. (c) Injunctive or declaratory relief.” [§1423(1)(a)–(c), lines 21–33]
→ Ties fines directly to model training budgets, creating a strong financial deterrent.
2. Voidance of liability-waiving clauses
– Any contract provision waiving RAISE Act liability is “void as a matter of public policy.” [§1423(2)(a), lines 34–38] and courts may pierce corporate veils to enforce penalties [§1423(2)(b), lines 39–48].
3. Cumulative remedies
– “Duties … are cumulative with any other duties … shall not be construed to relieve any party from any duties under other law” [§1424, lines 51–55].
Section E: Overall Implications
– The bill’s heavy-duty transparency, audit, and reporting regime focuses squarely on “frontier models” (i.e. the largest, most capable AI systems).
– Large AI vendors will face ongoing compliance costs (internal protocols, third-party audits, legal counsel for AG requests) likely running into millions annually.
– Startups approaching the “large developer” threshold must adopt formal safety‐by‐design processes early or face AG oversight.
– Academic research retains an exemption only if strictly “academic,” encouraging industry-academia partnerships to remain compliant.
– Residents gain whistleblower protections, encouraging in-house reporting of unsafe AI practices.
– By tying penalties to training compute spend, the state creates a financial disincentive for reckless frontier model releases, potentially slowing unfettered model proliferation.
– Regulators (the Attorney General) acquire broad investigatory powers, including access to unredacted safety protocols and audit reports, shifting a substantial compliance burden onto AI developers.
Potential Ambiguities
– “Unreasonable risk of critical harm” is not precisely quantified; developers may debate whether specific behaviors or dual-use scenarios cross that line [§1421(2)].
– The scope of “appropriately redacted” vs. “unredacted” disclosures to the AG may spur litigation over what counts as a “trade secret” vs. what the public or regulator must see [§1420(1), §1421(1)(c)].
In sum, the RAISE Act’s provisions are unmistakably AI-centric: they leverage compute‐cost thresholds, define AI-specific safety protocols, mandate transparency for frontier models, and enforce compliance through model-budget-based fines. It is designed to reshape New York’s AI landscape by imposing rigorous safety governance on the developers of the most powerful AI systems.
Senate - 6954 - Requires generative artificial intelligence providers to include provenance data on certain content made available by the provider
Legislation ID: 100093
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of S.6954 (the “Stop Deepfakes Act”). All observations are anchored to exact citations from the bill text.
Section A: Definitions & Scope
1. “Generative artificial intelligence system”
• Text: “Generative artificial intelligence system means a class of AI model that is self-supervised and emulates the structure and characteristics of input data to generate derived synthetic content…” (Sec. 1510(2), lines 4–11)
• Relevance: Explicitly targets AI systems that produce or modify content (images, video, audio, text).
2. “Generative artificial intelligence provider”
• Text: “Generative artificial intelligence provider means an organization or individual that creates, codes, substantially modifies, or otherwise produces a generative artificial intelligence system that is made publicly available for use by a New York resident…” (Sec. 1510(5), lines 14–18)
• Relevance: Captures developers and vendors of generative AI regardless of business model.
3. “Generative artificial intelligence hosting platform”
• Text: “Generative artificial intelligence hosting platform means an online repository or other website that makes a generative artificial intelligence system available for use…” (Sec. 1510(6), lines 19–25)
• Relevance: Covers marketplaces or websites distributing third-party AI models.
4. “Synthetic content”
• Text: “Synthetic content means audio, images or videos that have been produced or significantly modified by a generative artificial intelligence system.” (Sec. 1510(3), lines 9–12)
• Relevance: Defines the regulatory object—any AI-altered media.
5. Content provenance and related terms
• “Provenance data” (Sec. 1510(1), lines 2–9) – requires metadata tracking origin, edits, and AI usage.
• “Content provenance preservation” (Section heading of 1512).
• Scope statements: Sections 1511–1513 apply to AI providers, hosting platforms, social media, and state agencies.
Section B: Development & Research
The bill contains no direct provisions on funding, research reporting, or data-sharing mandates for AI R&D. Its focus is strictly on disclosure and labeling of AI-generated media.
Section C: Deployment & Compliance
1. Mandatory provenance tagging by AI providers
• Text: “A generative artificial intelligence provider shall apply provenance data … to synthetic content produced or modified by a generative artificial intelligence system that the generative artificial intelligence provider makes available.” (Sec. 1511(1), lines 45–49)
• Impact: Forces any vendor of a publicly accessible generative AI model to embed machine-readable or human-readable labels identifying AI usage.
2. Minimum required metadata
• Text: “The application of provenance data … shall, at a minimum, identify the digital content as synthetic and communicate … (a) that the content was created or edited using artificial intelligence; (b) the name of the generative artificial intelligence provider; (c) the time and date …; (d) the specific portions of the content that are synthetic; (e) the type of device…; and (f) any other additional provenance data specified in regulations …” (Sec. 1511(2), lines 50–6, page 3 lines 1–6)
• Impact: Standardizes metadata fields across providers—may require providers to alter internal pipelines, UI, and APIs.
3. Hosting platform obligations
• Text: “Generative artificial intelligence hosting platforms shall not make available a generative artificial intelligence system where the hosting platform knows that the generative artificial intelligence provider … does not apply provenance data … nor shall [it] deliberately prevent … application of provenance data…” (Sec. 1511(3), lines 7–15)
• Impact: Platforms like ModelHubs or online marketplaces must exert due diligence to delist or block non-compliant models.
4. Content preservation on social media
• Text: “A social media platform shall not delete, disassociate, or degrade … provenance data from … content uploaded … unless … required by law.” (Sec. 1512(1), lines 31–36)
• Impact: Ensures that once AI-tags are uploaded by users, platforms cannot strip them—protects integrity of provenance chain.
5. State agency publishing
• Text: “A state agency shall ensure … that all audio, images and videos published or distributed electronically … carry provenance data.” (Sec. 1513(1), lines 46–50)
• Impact: State-produced media must be labeled—even if not AI-generated—but metadata must record device, AI usage, provider name, etc.
Section D: Enforcement & Penalties
1. AI provider and hosting platform penalties (Sec. 1511(5), lines 21–30)
• Up to $100,000 per intentional or grossly negligent violation
• Up to $50,000 per unintentional/non-grossly negligent violation
2. Social media operator penalties (Sec. 1512(2), lines 37–44)
• Same tiered penalty structure for deleting or degrading provenance data
3. Attorney General rulemaking (Sec. 1514, lines 11–24)
• AG may adopt regulations on formats, methods, exceptions for tagging and preservation
4. Sunset clause
• Text: “Subdivisions 1, 2, 3 and 4 of section 1511, subdivision 1 of section 1512, and section 1513 … shall expire … December 31, 2030.” (Sec. 3, lines 25–29)
• Impact: This regime is temporary, suggesting a pilot or interim standard.
Section E: Overall Implications
• Advances transparency: By mandating standardized provenance metadata, the bill aims to curb misleading “deepfakes.”
• Compliance costs: AI startups and open-source distributors will need to integrate content-credentialing tech (e.g., C2PA) or risk delisting/fines.
• Hosting platform liability: Platforms hosting non-tagged models face enforcement risk, incentivizing stricter content review or model vetting.
• Social media and state agencies: Extends provenance preservation obligations beyond AI vendors to downstream publishers.
• Regulatory clarity & ambiguity:
– Clear on “synthetic content,” but “significantly modified” (Sec. 1510(3)) may require interpretive guidance (e.g., color correction vs. AI touch-up).
– “Type of device, system, or service” may be varied—AG rulemaking will be critical to narrow these categories.
• Temporary measure: The sunset in 2030 signals the legislature’s intent to revisit the framework as technology evolves.
In sum, S.6954 imposes a provenance-based disclosure regime specifically on generative AI systems and their distributors, and extends those requirements through the content supply chain (hosting platforms, social media, state agencies). While it does not directly fund or shape AI research, it introduces significant compliance obligations that could reshape how AI content is generated, labeled, and shared in New York.
Senate - 6955 - Establishes the artificial intelligence training data transparency act
Legislation ID: 100095
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a structured analysis of S.6955, the New York “Artificial Intelligence Training Data Transparency Act.” Each claim is anchored to the bill’s text.
Section A: Definitions & Scope
1. “Artificial intelligence” / “AI model” (§1421 (1), (8))
– Text: “Artificial intelligence or artificial intelligence technology means a machine-based system … that uses machine- and human-based inputs to perceive real and virtual environments …” (§1421 (1), lines 15–21).
– Relevance: This broad definition explicitly targets any “machine-based system” performing prediction, recommendation or decision tasks. By defining “AI model” as “a component of an information system … [that] implements AI technology” (§1421 (8), lines 23–27), the bill squarely encompasses both research prototypes and deployed AI-powered products.
2. “Generative artificial intelligence” (§1421 (3))
– Text: “Generative artificial intelligence means a class of AI models that are self-supervised and emulate the structure and characteristics of input data to generate derived synthetic content…”
– Relevance: The transparency obligations apply only to “generative” systems (e.g., large language models, image-synthesis tools).
3. “Developer” (§1421 (2))
– Text: “Developer means a person, partnership, state or local government agency, or corporation that designs, codes, produces, or substantially modifies an AI model or service for use by members of the public.”
– Scope: All private companies, academic spin-offs, and even public agencies fall under the transparency regime as long as they “design” or “substantially modify” a generative AI system for public use.
4. “Substantial modification” (§1421 (4))
– Text: “Substantial modification means a new version, new release, or other update … that materially changes its functionality or performance, including … retraining or fine tuning.”
– Implication: Even frequent patch-level fine-tuning triggers a new transparency filing.
5. “Train a generative … model or service” (§1421 (6))
– Text: “includes testing, validating, or fine tuning by the developer.”
– Impact: The reporting obligation captures internal ML workflows, not just initial training runs.
Section B: Development & Research
– §1422(1): Transparency of training data for public generative AI
• Text (§1422 (1), lines 28–36): “On or before January 1, 2026 … and prior to each time thereafter that a generative AI model or service … is made publicly available to New Yorkers … the developer shall post … documentation regarding the data used … including a high-level summary of the datasets …”
• Effect: Researchers and startups must prepare and publish dataset summaries (source, size, date ranges, licensing, presence of copyrighted or personal data, etc.) before any public demo or API launch in New York.
– Required data‐item breakdown (§1422 (1)(a)–(l), lines 37–67):
a) source/owner; b) how data furthers model purpose; c) data-point counts; d) types of data points or labels; e) copyright status; f) purchase/license status; g) inclusion of personal info; h) inclusion of aggregate consumer info; i) any data-cleaning or processing; j) collection time period; k) dates first used; l) use of synthetic data.
• Impact on R&D: Teams will need internal tracking systems for data provenance, labeling efforts, and cleaning logs. This could slow experimentation, especially for fine-tuning on dynamic datasets.
– Exemptions (§1422 (2))
• Text: “A developer shall not be required to post documentation … for any … model … whose sole purpose is the operation of aircraft in the national airspace; or … developed for national security, military, or defense purposes … only made available to a federal entity.”
• Interpretation: Civil-aviation and closed-door defense systems escape transparency. All other uses—even “closed beta” to New York institutions—are covered.
Section C: Deployment & Compliance
– Corporate employee data (§1423):
• Applicability (§1423 (1), lines 26–34): Any entity that “designs, codes, produces, or substantially modifies a generative AI model … using data of which a substantial part is derived from individuals employed or contracted by the entity … shall ensure … disclosure to each employee” of six items:
a) intended purpose; b) how data furthers that purpose; c) data-point types; d) inclusion of personal info; e) first use dates; f) collection period. (§1423 (1)(a)–(f), lines 35–46).
• Impact: Employers and AI labs must notify staff if they include staff emails, code commits, chat logs, or HR records in training.
– Exemptions mirror §1422 (2) (§1423 (2)(a)–(b), lines 50–57): aircraft operation and defense.
– Timing (effective date, §2): “This act shall take effect immediately.”
• Compliance: Any generative AI service already live must prepare disclosures by Jan 1 2026; employee notices for models in active use must go out immediately after enactment.
Section D: Enforcement & Penalties
– No explicit civil penalties or private right of action are provided in Article 44-B.
– Likely enforcement via New York’s consumer protection or false-advertising authorities under General Business Law. Absence of penalties creates uncertainty about “teeth” and compliance incentives.
Section E: Overall Implications
1. Transparency vs. Innovation
– Pros: Provides researchers, regulators, and the public a clearer view into training sources, enabling detection of copyright misuse, privacy breaches, or bias in training sets.
– Cons: The detailed data summaries impose administrative burdens—especially on small startups or academic labs with ad hoc data curation. They may hesitate to roll out new models to New York users.
2. Labor & Employment Effects
– Employee-data notice requirements recognize the growing practice of training on internal communications. This could improve workplace privacy but may also lead employers to exclude staff data entirely—or avoid using New York–based contractors.
3. Narrow Exemptions
– The aviation and defense carve-outs leave commercial and open-source AI completely within scope, ensuring robust oversight of civilian AI deployments.
4. Regulatory Precedent
– If enacted, New York becomes one of the first states to mandate training-data transparency for generative AI, likely influencing other jurisdictions.
5. Ambiguities
– “High-level summary” is undefined: providers may under-report or over-generalize (“1 billion tokens of mixed web data”).
– “Substantial part is derived from individuals employed…” could be read as requiring notices only when >50 percent of data comes from staff. The threshold is unclear.
In sum, S.6955 focuses on mandating dataset provenance disclosures for generative AI aimed at New Yorkers and requiring employee notices when staff-derived data are used. It tightens transparency, but its compliance costs and lack of clear enforcement mechanisms may chill some AI innovation in the state.
Senate - 7263 - Imposes liability for damages caused by a chatbot impersonating certain licensed professionals
Legislation ID: 113289
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused breakdown of Senate Bill S.7263, organized into the sections you requested. Every point is anchored to the bill text.
Section A: Definitions & Scope
1. “Artificial intelligence system” or “AI system”
• Citation: § 390-f(1)(a), lines 5–13
“Artificial intelligence system or AI system shall mean a machine-based system or combination of systems, that for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions…”
• Analysis: This is the bill’s core AI definition. It distinguishes AI systems from “basic computerized processes” (e.g., spellcheck, databases) (lines 11–18). By carving out routine software, the bill targets more sophisticated, learning-based models.
2. “Chatbot”
• Citation: § 390-f(1)(b), lines 19–22
“Chatbot shall mean an artificial intelligence system, software program, or technological application that simulates human-like conversation … to provide information and services to users.”
• Analysis: The bill singles out conversational AI—regardless of whether it uses large-scale neural networks, rule-based engines, or hybrids.
3. “Proprietor”
• Citation: § 390-f(1)(c), lines 23–30
“Proprietor shall mean any person, business … that owns, operates or deploys a chatbot system used to interact with users. Proprietors shall not include third-party developers that license their chatbot technology to a proprietor.”
• Analysis: Liability falls on the deployer/operator, not necessarily the third-party tech vendor.
Section B: Development & Research
There are no direct R&D-focused provisions (e.g., no funding mandates, reporting requirements, or data-sharing rules).
• Ambiguity: The definitions draw a line around “machine-based systems…that infer…how to generate outputs.” In theory, research prototypes outside deployment remain untouched.
• Interpretation: This bill concerns commercial and public deployers, not research labs or universities that do not interact with end-users via chatbots.
Section C: Deployment & Compliance
1. Prohibition on impersonating licensed professionals
• Citation: § 390-f(2)(a)(i), lines 4–12
“A proprietor of a chatbot shall not permit such chatbot to provide any substantive response … which, if taken by a natural person, would constitute a crime under … education law governing licensure … of certain professions.”
• Analysis: Deployers must configure chatbots to refuse or disclaim advice in fields like medicine, engineering, psychology, etc. They face liability if the bot “practices” these professions.
2. Unauthorized practice of law
• Citation: § 390-f(2)(a)(ii), lines 15–18
“Or would violate the provisions of article fifteen of the judiciary law prohibiting the practice or appearance as an attorney-at-law without being admitted and registered …”
• Analysis: Even non-lawyer chatbots cannot give legal advice as if they were attorneys.
3. Non-waivable liability
• Citation: § 390-f(2)(b), lines 19–21
“A proprietor may not waive or disclaim this liability merely by notifying consumers that they are interacting with a non-human chatbot system.”
• Analysis: A simple “I’m not a lawyer” disclaimer is insufficient to avoid liability.
4. Mandatory user notice
• Citation: § 390-f(4), lines 26–31
“Proprietors utilizing chatbots shall provide clear, conspicuous and explicit notice to users that they are interacting with an artificial intelligence chatbot program… in the same language the chatbot is using and in a size … no smaller than the largest font size of other text …”
• Analysis: This strengthens transparency mandates and may require UI/UX changes.
Section D: Enforcement & Penalties
1. Civil action for actual damages
• Citation: § 390-f(3), lines 21–25
“A person may bring a civil action to recover actual damages and, if it is found that such proprietor has willfully violated this section, the violator shall be liable for actual damages together with costs and reasonable attorneys fees and disbursements …”
• Analysis: End-users (or possibly regulators acting in parens patriae) can sue. Willfulness raises potential for greater litigation risk.
2. No criminal penalties specified
• Observation: The bill calls prohibited conduct a “crime” only by analogy to existing education law, but imposes only civil liability here.
Section E: Overall Implications
1. Restrictive effect on commercial chatbot deployment
• Any vendor offering advice in regulated domains (medical, legal, engineering, finance if regulated) must implement guardrails to avoid unlicensed practice.
• Startups may face elevated compliance costs—requiring prompt detection of user intent and dynamic filtering.
2. Transparency demands
• Promotes user trust but increases UI complexity.
• Could spur a market for “AI-notice” compliance tools.
3. Litigation risk
• The “willful violation” standard plus attorneys’ fees elevates the stakes for proprietors, possibly chilling deployments in sensitive domains.
4. Research & innovation
• The bill is narrowly focused on deployed chatbots and does not hinder underlying AI research or non-chatbot applications.
5. Regulatory clarity vs. ambiguity
• The definitions carve out trivial software, but “infers…how to generate outputs” could encompass broader classes of AI (e.g., recommendation engines).
• States might interpret “substantive response” differently, leading to uneven compliance burdens.
In sum, S.7263 addresses liability, transparency, and user protection for AI-based chatbots in professional contexts, but leaves general AI R&D largely untouched.
Senate - 7599 - Relates to automated decision-making by government agencies
Legislation ID: 128379
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of S.7599 (2025-2026), organized in five sections. Every point cites the bill text directly.
Section A: Definitions & Scope
1. “Automated decision-making system” (Sec. 501(1), lines 11–18)
– “any software that uses algorithms, computational models, or artificial intelligence techniques … to automate, support, or replace human decision-making … without meaningful human discretion.”
– Explicitly targets AI-driven systems (e.g. those employing “machine learning algorithms”).
– Excludes “basic computerized processes … that do not materially affect … an individual” (lines 18–23), thereby focusing on high-impact AI.
2. “Meaningful human review” (Sec. 501(2), lines 1–7)
– Defines the required human oversight capabilities (“authority to intervene or alter the decision … recommended or made by the automated system”).
3. “Government agency” (Sec. 501(3), lines 8–18)
– Broadly includes all state & local bodies, public authorities, schools, etc.
4. “Public assistance benefit” (Sec. 501(4), lines 19–27)
– Covers any state-controlled benefit (cash assistance, housing assistance, unemployment benefits, etc.).
– Carves out purely federal programs.
Section B: Development & Research
There are no direct R&D mandates, funding provisions, or data-sharing obligations. The bill:
– Imposes no grant requirements or facility for AI research.
– Requires agencies to commission or conduct “impact assessments” (Sec. 503(1), lines 47–53) before deploying or materially changing any AI system.
– Agencies must re-assess every two years or upon material change (Sec. 503(1), line 54–Sec. 503(2), line 1).
Section C: Deployment & Compliance
1. Prohibition without human review (Sec. 502(1), lines 29–36)
– “No government agency … shall utilize … any automated decision-making system … that will have a material impact on … the rights, civil liberties … of any individual … unless … subject to continued and operational meaningful human review.”
– Covers delivery of “public assistance benefit” or any function affecting statutory or constitutional rights.
2. Procurement ban (Sec. 502(2), lines 38–46)
– “No government agency shall authorize any procurement … of any service or system utilizing … automated decision-making … unless … meaningful human review.”
3. Impact assessments (Sec. 503, lines 47–56 & 1–7)
– Must include objectives, algorithm summaries (503(1)(c)(i)–(ii), lines 8–14), bias testing & mitigation (503(1)(d)(i), lines 15–21), cybersecurity/privacy risks (503(1)(d)(ii), lines 22–25), public safety risks (503(1)(d)(iii), lines 26–28), misuse scenarios (503(1)(d)(iv), lines 29–31), data sensitivity (503(1)(e), lines 31–35), and user notification procedures (503(1)(f), lines 36–39).
– Agencies must stop use if discriminatory outcomes are found (503(2), lines 40–44).
4. Transparency (Sec. 504, lines 45–56 & 1–4)
– Impact assessments go to governor and legislature 30 days before implementation (504(1), lines 45–49).
– Must be published online, though narrowly redactable for safety or privacy (504(2)(b)–(c), lines 52–56 & following).
Section D: Enforcement & Penalties
– The sole enforcement mechanism: agencies must “cease any utilization … of such automated decision-making system” if an impact assessment “finds … discriminatory or biased outcomes” (Sec. 503(2), lines 42–44).
– There is no civil or criminal penalty specified; compliance is ensured via procurement and deployment bans.
– Non-compliance could be challenged administratively or potentially via injunction, but the bill itself does not create fines.
Section E: Overall Implications
– Advances transparency and human oversight: tightens the bar for any AI system that materially affects New Yorkers’ rights or benefits.
– Likely slows or discourages government adoption of opaque AI, especially from vendors unable to provide sufficient documentation, bias-testing or human-in-the-loop guarantees.
– Imposes recurring compliance costs (impact assessments every two years; pre-deployment legislative notices).
– Benefits vendors prepared to supply explainable models and auditing tools; raises barriers for black-box AI.
– Researchers and startups working with agencies must factor in lengthy review cycles.
– No R&D incentives, but the bill could spur private-sector innovation in AI audit, bias-mitigation, and human-in-the-loop platforms.
Senate - 8115 - Relates to the use of automated decision tools by banks for the purposes of making lending decisions
Legislation ID: 145684
Bill URL: View Bill
Sponsors
Senate - 822 - Relates to the disclosure of automated employment decision-making tools and maintaining an artificial intelligence inventory
Legislation ID: 66402
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of S.822 (2025–2026) organized as requested. All quotations reference section and line numbers from the bill as printed in your prompt.
Section A: Definitions & Scope
1. “Automated employment decision-making tool” (AI subset)
– Citation: §401(1), lines 3–13
“ ‘Automated employment decision-making tool’ shall mean any software that uses algorithms, computational models, or artificial intelligence techniques … to materially automate … human decision-making … regarding employment …”
– Relevance: By invoking “artificial intelligence techniques,” this definition explicitly targets AI-powered HR systems (e.g., resume screeners, promotion-recommendation models).
– Scope: It excludes “basic calculators, spellcheck tools, … or any tool … that do not materially affect … rights, liberties … of any individual” (lines 13–19), narrowing coverage to AI with significant personal impact.
2. “Artificial intelligence system” (broader AI inventory)
– Citation: §103-e(2), lines 50–55; lines 1–7 on p. 4
“ ‘Artificial intelligence system’ shall mean a machine-based system that … make predictions, recommendations, or decisions influencing real or virtual environments that … may ‘directly impact the public.’ … includes … machine learning, large language model, natural language processing, and computer vision …”
– Relevance: This is a catch-all definition for any AI used by state agencies, from sophisticated LLMs to vision models.
– Ambiguity: The phrase “when used, may ‘directly impact the public’ ” (§103-e(2), lines 52–54) could be read broadly (any public-facing AI) or narrowly (only those altering rights or safety), leaving implementation open.
3. “Directly impact the public”
– Citation: §103-e(3), lines 10–17
“ ‘Directly impact the public’ shall mean when the use of an AI system would control, have a material impact on, or meaningfully influence … activities that impact the safety, welfare, or rights of the public. … decisions about … housing, hiring …”
– Scope note: This anchors the inventory requirement to AI with tangible consequences, excluding back-office automation.
Section B: Development & Research
There are no provisions in S.822 that allocate funding, mandate data-sharing, or otherwise directly promote AI research. Instead, the bill focuses on transparency and workforce protections.
Section C: Deployment & Compliance
1. Disclosure of AI‐based employment tools
– Citation: §402, lines 21–34
“Any state agency that utilizes an automated employment decision-making tool … shall publish a list … on such state agency’s website … annually. Such disclosure shall include: 1. a description of the tool; 2. the date … began; 3. a summary of purpose and use; and 4. any other information deemed relevant …”
– Impact: Agencies must track and publicly list their AI hiring/HR tools, increasing visibility for applicants and watchdogs.
2. State‐wide AI inventory
– Citation: §103-e(1), lines 37–46; §103-e(4), lines 21–23
“The office shall maintain an inventory of state agency AI systems. The inventory … posted … on the open data website … annually. State agencies shall submit … at least sixty days … The office may withhold information … if … it would jeopardize security …”
– Impact: Creates a centralized, public register of government-used AI. Vendors will know their products are potentially subject to disclosure; agencies must build new reporting processes.
– Ambiguity: The “withhold … if … jeopardize security” clause (§103-e(1), lines 46–48) gives the Chief Technology Officer discretion over what remains secret.
3. Protection of employee rights
– Citation: Civil Service Law §80(10)(a–c), lines 28–47
“The use of AI systems … shall not affect (i) existing rights … under a collective bargaining agreement …; (b) result in … discharge, displacement … or impairment of existing collective bargaining agreements; or (ii) transfer of existing duties … to an AI system …”
– Impact: Bars agencies from deploying AI in ways that cut jobs or alter union terms, effectively limiting use cases in HR and operations.
Section D: Enforcement & Penalties
– No explicit monetary penalties or fines are attached.
– Compliance is enforced administratively:
• Failure to disclose under §402 may lead to public and legislative scrutiny.
• Annual submission requirements (§103-e(1)) imply that omission could be treated as a reporting violation under the State Technology Law enforcement regime (which generally relies on agency oversight rather than criminal penalties).
– Ambiguity: The bill does not specify sanctions, leaving enforcement to existing oversight bodies (Office of Information Technology Services, civil service commissioners).
Section E: Overall Implications
1. Transparency and Accountability
– By mandating public disclosure (§402) and a centralized inventory (§103-e), S.822 aims to shine a light on AI in government. This can deter misuse and inform stakeholders (researchers, civil-rights groups).
2. Chilling Effect on Procurement
– The reporting burdens and no-job-loss provisions may discourage agencies from piloting innovative AI, especially in HR and administrative functions. Start-ups and vendors might face slow sales cycles as agencies build compliance processes.
3. Worker Protections
– Civil Service Law amendments (§80(10)) secure current employees’ rights but could block efficiency gains from automation. Unions may welcome these safeguards.
4. Limited R&D Support
– There is no counterpart section offering grants, data-sharing, or innovation sandboxes. The focus is purely on guardrails, not on promoting research or private-sector AI growth.
5. Implementation Ambiguities
– Key terms like “directly impact the public” (§103-e(3)) and authority to “withhold” data for “security” (§103-e(1)) are open to interpretation and may lead to uneven application across agencies.
In sum, S.822 builds transparency and union-friendly guardrails around AI in New York state government but stops short of fostering AI research or clarifying enforcement. It will require agencies to stand up inventory and disclosure processes, with potential downstream effects on procurement and vendor engagement.
Senate - 8331 - Enacts the "New York artificial intelligence transparency for journalism act"
Legislation ID: 159844
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of the proposed “New York artificial intelligence transparency for journalism act,” organized as requested. All page and section citations refer to the bill as introduced (LBD13206-03-5).
Section A: Definitions & Scope
1. “Artificial intelligence” (AI) (Sec. 338.1, lines 32–38)
• “Artificial intelligence means a machine-based system that can…make predictions, recommendations, or decisions…uses machine and human-based inputs…abstract such perceptions into models…formulate options….”
∘ Pertains to any “machine-based system” whose behavior is at least partly automated via learned models.
2. “Generative artificial intelligence” (Sec. 338.6, lines 9–12)
• “Generative artificial intelligence means a class of artificial intelligence models that emulate…input data to generate…synthetic content, including…images, videos, audio, text, and other digital content.”
∘ Targets “large language models,” image-and-video synthesizers, etc.
3. “Developer” (Sec. 338.5, lines 4–9)
• “Developer means a person that designs, codes, produces, or substantially modifies an artificial intelligence system or service for use by members of the public. The term ‘developer’ shall not include artificial intelligence systems used, developed or obtained by a journalism provider for internal use.”
∘ Explicitly focuses on public-facing AI tools, excluding in-house newsroom tools.
4. “Artificial intelligence utilization” (Sec. 338.9, lines 19–25)
• “Artificial intelligence utilization means to use digital content as data to develop the capabilities of a generative artificial intelligence system…includes…the initial dataset training [and] further testing, validating, grounding, or fine tuning.”
∘ Defines the full pipeline of training/fine-tuning.
5. “Crawler” (Sec. 338.4, lines 1–4)
• “Crawler means software that accesses content from a website…such as an online crawler, spider, fetcher, client, bot….”
∘ Identifies the scraping technology most AI developers use to ingest journalistic content.
6. “Covered publication” and “Journalism provider” (Sec. 338.3, lines 41–47 & 13–17)
• Defines precisely which news outlets are protected by this act (print, broadcast, digital, periodic, error-corrected, updated at least monthly, and carrying media liability insurance).
Section B: Development & Research
No provisions mandate AI R&D funding or public-sector research partnerships. However, transparency requirements will directly affect how developers collect and prepare data during model development:
1. Training-data disclosure (Sec. 338-a.1.a, lines 25–35)
• “On or before January 1, 2027 … the developer … shall post … regarding video, audio, text and data from a covered publication used to train the generative artificial intelligence system or service: (i) the URLs accessed by crawlers…; (ii) a detailed description of the…data…sufficient to identify individual works; (iii) whether any source identifiers…were removed; and (iv) the timeframe of data collection.”
∘ Researchers and startups must catalog and publicly reveal their provenance logs, potentially raising IP and competitive-secrecy concerns.
2. Crawler-identity disclosure (Sec. 338-a.2.a, lines 1–9 on p 4)
• “On or before January 1, 2027, the developer … who deploys a crawler…shall disclose …: (i) the name of the crawler including the crawler’s IP address and specific identifier…; (ii) the legal entity responsible; (iii) the specific purposes; (iv) the legal entities to which scraped data is provided; and (v) a single point of contact…”
∘ Imposes operational overhead on research teams, requiring them to maintain public “crawler registries” and points of contact.
Section C: Deployment & Compliance
1. Public-facing transparency website (Sec. 338-a.1.a, lines 25–31)
• Developers “shall post on the developer’s internet website” all required data-use details.
∘ All commercial AI systems offered to New Yorkers will need a dedicated “Transparency” page listing training-source details.
2. Exemption for licensed agreements (Sec. 338-a.1.b, lines 47–52)
• “The information…shall not be required where there is an express written agreement authorizing the developer to access the journalism provider’s content and the parties agree not to post information…on the developer’s website.”
∘ Creates an incentive for developers to negotiate direct licensing with publishers.
3. Non-interference with SEO (Sec. 338-a.2.c, lines 18–21)
• “The exclusion of a crawler by a website operator shall not negatively impact the findability of the website operator’s content in a search engine.”
∘ Prevents sites from blocking AI crawlers without harming their own SEO—encourages compliance rather than forced blocking.
Section D: Enforcement & Penalties
1. Subpoena power (Sec. 338-b.1.a–c, lines 21–51 on p 4–5)
• A journalism provider may request a Supreme Court clerk to issue a subpoena for:
– “URLs accessed…dates and times of collection” (i)
– “Text and data used for artificial intelligence utilization…type, provenance, means…when” (ii)
• Developer must comply within 30 days or face penalties under CPLR 2308.
∘ Empowers publishers to compel private-sector disclosure of model-training records.
2. Injunctions & statutory damages (Sec. 338-b.2.a–b, lines 53–61 on p 5)
• “A journalism provider may bring an action…for an injunction to compel a developer to comply…”
• “If the court finds that the developer did not comply…shall order compliance and may impose statutory damages…of up to ten thousand dollars.”
∘ Creates a modest per-developer sanction and injunctive path.
3. Attorney General enforcement back-stop (Sec. 338-b.2.c, lines 8–12 on p 5)
• If a developer fails to obey a court order, the publisher “may request that the attorney general bring an action on their behalf…to ensure compliance…and any statutory damages assessed.”
Section E: Overall Implications
• Transparency vs. Trade Secrets: Requiring public posting of precisely which URLs and content were used (Sec. 338-a.1.a.(i)–(iv)) could undermine model-provenance secrecy and discourage new entrants lacking direct licensing deals. The protective-order carve-out for subpoenas (Sec. 338-b.1.b) may only partially shield proprietary dataset structures.
• Incentive for Licensing: By exempting licensed-content deals from public posting (Sec. 338-a.1.b), the bill pushes developers toward negotiation and compensation arrangements with journalism providers.
• Operational Overhead: All AI developers serving New York users must maintain up-to-date crawler registries, transparency websites, and complaint contacts (Sec. 338-a.2.a). This burdens small startups and open-source projects more heavily than large incumbents.
• Enforcement Levers: Publishers gain new legal tools (subpoena, injunction, AG enforcement) to extract provenance data or force compliance, reshaping power dynamics between media and AI firms.
• Ambiguities & Scope Gaps: The definition of “access” (Sec. 338.2, lines 39–43) is broad (“obtain, retrieve…reproduce”), leaving open whether ephemeral API calls or browser-based previews must be tracked. The term “publicly available to New Yorkers” (Sec. 338-a.1.a) may require geofencing disclosures only for state-based traffic.
In sum, this bill does not ban or directly regulate AI capabilities but imposes detailed provenance and crawler-disclosure rules on any generative AI system or service offered to New York users, backed by subpoena and injunction remedies to journalism providers. It is likely to accelerate licensing negotiations, raise compliance costs (especially for small developers), and shift industry norms around AI training-dataset transparency.
Senate - 8420 - Relates to requiring advertisements to disclose the use of a synthetic performer
Legislation ID: 168346
Bill URL: View Bill
Sponsors
Senate - 8459 - Prohibits transcripts being made from video conference meetings by artificial intelligence without conspicuous disclosure during such meeting
Legislation ID: 215695
Bill URL: View Bill
Sponsors
Senate - 933 - Establishes the position of chief artificial intelligence officer
Legislation ID: 66546
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. “Artificial intelligence” or “AI” (Bill § 1, New § 101.7, lines 3–12)
– “a machine-based system that … may influence physical or virtual environments” (lines 3–8).
• Relevance: Explicitly targets any autonomous or adaptive software “designed to sense, interpret, process … data” (lines 10–15) and “to generate options, recommendations, … outputs that influence physical or virtual environments” (lines 17–21).
• Scope: Excludes “basic computerized processes … that do not materially affect the rights, liberties, safety or welfare of any human” (lines 4–9).
2. “Automated decision-making system” (Bill § 1, New § 101.8, lines 10–19)
– “any software that uses algorithms, computational models, or artificial intelligence … to automate, support, or replace human decision-making” (lines 10–14).
• Relevance: Captures rule-based and machine-learning systems that produce “conclusions, recommendations, outcomes … predictions” (lines 15–17).
• Scope: Similarly excludes “basic computerized processes … that do not materially affect the rights, liberties, safety or welfare of any human” (lines 18–22).
Section B: Development & Research
1. Risk Management & Testing (Bill § 2, § 102-a(2)(g), lines 32–40)
– “Study the implications of the usage of artificial intelligence for data collection … develop common metrics to assess trustworthiness … minimize performance problems and unanticipated outcomes” (lines 32–38).
• Impact: Mandates standardized evaluation frameworks, likely incentivizing research on AI safety metrics and validation procedures. Researchers may be asked to align studies with state-defined “metrics to assess trustworthiness.”
2. Reporting Requirements (Bill § 2, § 102-a(2)(h), lines 41–49)
– Annual report “on progress, findings, studies and recommendations regarding the use of artificial intelligence and automated decision-making systems” (lines 41–45), publicly posted unless redacted for privacy or security (lines 47–52).
• Impact: Creates transparency obligations for state agencies, potentially spurring collaborations with academic labs to collect and analyze usage data. Ambiguity: “substantial negative impact on health or safety” (line 47) may allow broad redactions.
Section C: Deployment & Compliance
1. Policy & Governance Handbook (Bill § 2, § 102-a(2)(a)(ii), lines 44–49)
– “Developing and updating a handbook regarding the use, study, development, evaluation, and procurement of systems that use artificial intelligence … for use by the state’s departments, boards …” (lines 44–49).
• Impact: Establishes standardized best practices for procurement and deployment; vendors will need to certify compliance with the state handbook.
2. Human Oversight Standards (Bill § 2, § 102-a(2)(a)(iv), lines 1–3)
– “Setting governance standards for human oversight of artificial intelligence and automated systems, and determining resource requirements for responsible adoption” (lines 1–3).
• Impact: Could require vendors to embed “human-in-the-loop” controls. Startups and established vendors must demonstrate adherence in proposals or risk rejection.
3. Audits & Investigations (Bill § 2, § 102-a(2)(i), lines 1–13)
– “Investigate and conduct periodic audits … to ensure … tools or systems comply with … laws; … benefits outweigh risks; … secure, protected and resistant to … manipulation” (lines 1–11).
• Impact: Introduces enforcement of technical security and fairness requirements. Regulators may demand proof of bias testing, security penetration results, or risk assessments.
• Exemption Clause (lines 12–21) clarifies audits do not “restrict … access to … internal investigation … prevent, detect, protect, respond” to security incidents.
Section D: Enforcement & Penalties
1. Recommendations to Deactivate (Bill § 2, § 102-a(2)(f), lines 26–31)
– “Recommend the replacement, disconnection or deactivation of any application … that demonstrates … deployment is inconsistent with provisions of law or is otherwise harmful” (lines 26–31).
• Impact: Creates a non-binding but politically weighty mechanism to halt harmful AI uses. Ambiguity: “harmful to the operations of the state” could be broadly interpreted.
2. Authority to Request Resources (Bill § 2, § 102-a(3), lines 23–28)
– Chief AI Officer “may request and receive … staff and other assistance, information, and resources …” (lines 23–28).
• Impact: Empowers the office to enforce compliance through resourcing investigations or audits.
3. Advisory Committee Input (Bill § 3, § 104-a(5)(j), lines 36–38)
– Advisory committee “Make periodic recommendations to the legislature on legislative or regulatory changes.” (lines 36–38).
• Impact: Encourages ongoing legislative refinement; potential for future penalties or incentives.
Section E: Overall Implications
1. Centralized Oversight & Standardization
– By establishing a Chief AI Officer (§ 102-a(1), lines 25–33) and advisory committee (§ 104-a(1), lines 31–39), the bill creates a centralized governance structure for AI in New York. Vendors and researchers will face a uniform set of policies rather than siloed agency rules.
2. Encouragement of Responsible Innovation
– Risk management plans (§ 102-a(2)(a)(iii), lines 49–55) and public transparency (§ 102-a(2)(h), lines 41–49) can foster public trust but add reporting burdens. Startups may need dedicated compliance functions.
3. Potential Chilling Effects
– Mandated audits and deactivation recommendations (§ 102-a(2)(f),(i), lines 26–31, 1–11) could dissuade agencies from piloting novel AI solutions or vendors from proposing cutting-edge models that carry ambiguous risks.
4. Ambiguities & Interpretations
– Exclusions for “basic computerized processes” (Defs § 101.7(b), § 101.8, lines 4–9, 18–22) hinge on “material” effects on rights or welfare; interpreting “material” may generate disputes over what requires oversight.
5. Regulatory Evolution
– Advisory committee’s mandate to recommend legislative changes (§ 104-a(5)(j), lines 36–38) means the AI regulatory framework will likely evolve, providing opportunities for stakeholder input but also uncertainty for long-term projects.
Senate - 934 - Requires warnings on generative artificial intelligence systems
Legislation ID: 66547
Bill URL: View Bill
Sponsors
Pennsylvania
House - 1533 - An Act amending Title 18 (Crimes and Offenses) of the Pennsylvania Consolidated Statutes, in culpability, providing for liability for deployment of artificial intelligence system.
Legislation ID: 194122
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an analysis of House Bill 1533 (PN 1794), “Liability for deployment of artificial intelligence system,” organized into the five sections you requested. Every claim is anchored to direct quotations from the bill text. Where language is ambiguous, I note possible interpretations.
Section A: Definitions & Scope
1. “Artificial intelligence system” (lines 24–31)
– Quotation: “A machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations or decisions influencing real or virtual environments, including the ability to: (i) Perceive real and virtual environments. (ii) Abstract perceptions… into models… (iii) Use model inference….” (lines 25–30)
– Relevance: This broad definition targets virtually any AI that uses perception, model-building, or inference. It explicitly includes “generative artificial intelligence and any substantially similar technology yet to be developed.” (line 31)
– Implication: Startups and established vendors will need to determine if their systems fall under this sweeping definition, including edge‐deployed and cloud‐based solutions.
2. “Deployment” (lines 7 –15 on page 3)
– Quotation: “The use of an artificial intelligence system in a manner that has the potential to affect external persons, systems or legal interests… includes commercial implementation, enterprise use, individual use or the use of autonomous systems affecting third parties.” (lines 8–13)
– Relevance: “Deployment” covers nearly any real-world use beyond lab research, automatically capturing consumer apps, autonomous vehicles, robotic systems, cloud services, and even individual hobbyists.
– Ambiguity: The phrase “purely experimental, nondeployed research models… reasonably secured to prevent unauthorized access” (lines 14–17) is vague. What qualifies as “reasonable” security isn’t defined, leaving open interpretive room for enforcement agencies.
Section B: Development & Research
No provisions in this bill target AI research funding, data-sharing requirements, or reporting obligations. The sole focus is on liability after deployment. Researchers working exclusively on non-deployed, secured prototypes would likely sit outside the bill’s scope by design, but real‐world university testbeds or field trials risk triggering “deployment” provisions.
Section C: Deployment & Compliance
1. Strict liability for negative outcomes (lines 10–14)
– Quotation: “A person that engages in the deployment of an artificial intelligence system … shall be subject to criminal or civil liability, or both, for any negative outcome… including: (1) Physical harm… (2) Economic misconduct… (3) Unlawful data scraping… (4) Discriminatory… decision making… (5) False, deceptive… statements… (6) Any other act that, if committed by a person, would constitute a violation of law or amount to tortious conduct.” (lines 10–18)
– Impact: Vendors and users of AI must anticipate and insure against a broad swath of harms. This could chill aggressive AI deployments, raise compliance costs, and spur the purchase of specialized liability insurance.
2. Preservation of common-law duty of care (lines 15–18)
– Quotation: “Automating an action does not remove a person’s general duty of care relating to the action as established under common law.” (lines 15–18)
– Impact: Courts will continue to apply negligence and tort doctrines to AI contexts, but the bill adds a statutory overlay that could lead to parallel or overlapping suits.
3. Bar on avoiding liability via “autonomy” claims (lines 19–27)
– Quotation: “A person may not avoid liability… by asserting that: (i) The artificial intelligence system acted autonomously… (ii) The outcomes… were unintended, unforeseen or the result of machine-learning adaptation. (iii) The system was trained… by a third party… (iv) The system was marketed, certified or believed to be ‘safe,’ ‘self-regulating’ or ‘autonomous.’” (lines 19–27)
– Impact: Downstream users cannot shift blame to AI developers or vendors claiming black‐box autonomy. This pushes all deployers to maintain oversight and documentation.
4. Affirmative defenses (lines 28–42)
– Quotation: “A person may assert a defense… by demonstrating that: (i) the person implemented reasonable, ongoing oversight, safeguards and fail-safe mechanisms… or (ii) the harmful act… resulted solely from an unforeseeable and unauthorized interference by an external actor… with previously implemented reasonable security measures.” (lines 28–42)
– Impact: Businesses will need to invest in compliance programs, auditing, incident-response plans, and security controls. Those investments could benefit larger firms more than small startups that lack resources.
Section D: Enforcement & Penalties
– The bill provides for “criminal or civil liability, or both” (line 13) but does not specify fine ranges or imprisonment terms.
– Enforcement will likely fall to existing state civil courts (for tort suits) and criminal prosecutors under Pennsylvania’s general criminal statutes, invoking this new section (§ 306.1) as the underlying wrongful act.
– Ambiguity: Because the bill does not define penalty levels, courts must determine appropriate sanctions case by case, which may lead to inconsistent outcomes or push regulators to seek follow-on guidance.
Section E: Overall Implications
– Restrictive liability regime: By imposing near-strict liability on deployers, the bill could slow adoption of advanced AI applications in critical sectors (transportation, healthcare, finance) unless robust oversight frameworks are in place.
– Incentivizes compliance programs: Entities will likely build dedicated AI risk-management teams, compliance checklists, and routine audits to qualify for the affirmative defenses.
– Unequal impact: Well-capitalized firms can absorb compliance and insurance costs, but startups and academic spinoffs may struggle, potentially consolidating AI deployment in the hands of established vendors.
– Legal clarity vs. uncertainty: The broad definitions give clarity on coverage but leave open questions around “reasonable” safeguards, “purely experimental” work, and precise penalties—inviting litigation to fill in the gaps.
In sum, HB 1533 signals Pennsylvania’s intent to regulate AI deployment closely by tying liability explicitly to automated decision-making systems. The bill’s strength lies in its comprehensive scope, but its ambiguity around defenses, penalty structures, and security standards may require follow-on rulemaking or judicial interpretation.
House - 317 - An Act amending Title 18 (Crimes and Offenses) of the Pennsylvania Consolidated Statutes, in computer offenses, providing for artificial intelligence; and imposing a penalty.
Legislation ID: 13169
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused breakdown of House Bill 0317 (2025-2026), organized per your requested structure. All citations refer to the official section (§) and, where needed, line numbers from the printer’s copy.
Section A: Definitions & Scope
1. “Artificial intelligence” (§ 7681, lines 1–3)
• Text: “Artificial intelligence. Technology or tools that use predictive algorithms to create new content, including audio, code, images, text, simulations, videos and the likeness of another individual.”
• Relevance: This is the bill’s core definition of AI. By focusing on “predictive algorithms” and enumerating modalities (audio, code, images, etc.), it encompasses virtually all modern generative AI systems (e.g., large language models, image synthesis, deep-fake tools).
• Ambiguity: “Predictive algorithms” could be read narrowly (only machine-learning models) or more broadly (any statistical tool). It does not explicitly exclude rule-based chatbots, which may create “new content.”
2. “Artificial Intelligence Generated Material” (§ 7681, lines 4–6)
• Text: “Artificial Intelligence Generated Material. An image, text or video that used artificial intelligence in whole or in part to create the image, text, simulation or video.”
• Relevance: Defines the covered output. By including “in whole or in part,” the bill covers any human–AI collaboration.
3. “Watermark” (§ 7681, lines 6–9)
• Text: “Watermark. A mark placed on an artificial intelligence generated image, simulation or video.”
• Relevance: Establishes the remedy (watermark) for transparency. Note that “text” is not mentioned here, although § 7682 later requires marking text. This omission is ambiguous.
Section B: Development & Research
– No provisions directly address funding, research reporting, data sharing, or university activity.
– Implication: The bill does not advance or restrict AI R&D. Researchers remain unaffected unless they distribute AI-generated content without watermarking.
Section C: Deployment & Compliance
1. Watermark Requirement (§ 7682, lines 1–9)
• Text: “An individual who creates or distributes an image, text, simulation or video using artificial intelligence shall place a watermark on 30% of the image, text or video, to which the following shall apply:
(1) The watermark shall have a minimum of 50% opacity.
(2) The watermark shall contain the following statement: ‘Artificial Intelligence Generated Material.’”
• Relevance: Any publisher, creator, or distributor of AI content (commercial or nonprofit) must overlay a semi-opaque label over 30% of the medium.
• Potential Impact:
– Startups and content platforms will need to build watermarking tools into their pipelines to avoid fines.
– User-generated content sites (social media, forums) may need to filter or auto-stamp posts.
– Ambiguity: “30% of the image, text or video” is not defined—does it mean area, duration, word count? Different interpretations could lead to uneven enforcement.
2. Exceptions (§ 7683, lines 1–8)
• Text: “Film or television show productions … shall be exempt … if …
(1) Use of artificial intelligence for visual effects does not involve the use of an individual.
(2) If … involves … an individual, the individual has provided written consent to create a likeness…”
• Relevance: Exempts VFX in professional film/TV if no human likeness is involved, or if all likenesses are cleared.
• Impact:
– Major studios and VFX houses can avoid labeling crowd scenes or CGI landscapes.
– Indie filmmakers must track consent forms for any AI-generated likeness.
– Broadly, the exemption is narrow—applies only to “film or television show productions created or distributed in this Commonwealth.”
Section D: Enforcement & Penalties
1. Penalty Structure (§ 7684, lines 1–8)
• Text: “An individual who violates section 7682 … commits a misdemeanor of the second degree and shall be subject to a $1,000 fine for a first offense. An individual who commits a second or subsequent violation … within a five-year period … shall be subject to a $10,000 fine.”
• Relevance: Creates a criminal misdemeanor for failure to watermark.
• Impact:
– Regulatory agencies (e.g., state attorney) will need to monitor AI content distribution.
– Small creators risk criminal charges and fines; might self-censor or avoid AI tools.
– Startups may face insurance or compliance costs to manage labeling infractions.
Section E: Overall Implications
• Transparency vs. Burden: The bill mandates clear disclosure of AI-generated content, enhancing consumer awareness and potentially limiting deception (deep fakes, fake news).
• Compliance Costs: Watermarking at 30% opacity with 50% coverage is technically onerous—especially on text and dynamic media. Platforms will need automatic tools and audits.
• Enforcement Uncertainty: The misdemeanor framework could deter casual use of AI tools. Ambiguous terms (“30% of text”) may lead to inconsistent enforcement or litigation.
• Market Effects:
– Established vendors may adapt quickly, embedding watermarks in their APIs; smaller developers and hobbyists may struggle.
– Researchers and nonprofits distributing AI outputs (e.g., academic demos) must comply or risk misdemeanor charges.
• Legal Precedent: If enacted, Pennsylvania would be among the first states to criminalize unwatermarked AI content, possibly spurring similar bills elsewhere—or federal preemption efforts.
In sum, HB 0317 explicitly targets AI-generated media via mandatory watermarking, backed by misdemeanor penalties, but does not address AI research, testing, model governance, or broader liability frameworks. The definitions are broad, and key terms like “30% of text” remain open to interpretation—raising compliance and enforcement challenges for all stakeholders.
House - 317 - An Act amending Title 18 (Crimes and Offenses) of the Pennsylvania Consolidated Statutes, in computer offenses, providing for artificial intelligence; and imposing a penalty.
Legislation ID: 192947
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a structured analysis of House Bill 317 (2025), which adds Subchapter F (“Artificial Intelligence”) to Chapter 76 of Title 18 (Crimes and Offenses) of the Pennsylvania Consolidated Statutes. Every claim is anchored to quoted text from the draft bill.
Section A: Definitions & Scope
1. “Artificial intelligence” (AI)
• Text (§ 7681, lines 2–5):
“ ‘Artificial intelligence.’ Technology or tools that use predictive algorithms to create new content, including audio, code, images, text, simulations, videos and the likeness of another individual.”
• Relevance: This is the core definition. It explicitly targets any system that “use[s] predictive algorithms to create new content,” covering generative AI models (e.g., image and text synthesis).
• Ambiguity: “Predictive algorithms” is broad—could include basic statistical models. Startups may question whether autocomplete or simple pattern-matching tools fall under this law.
2. “Artificial Intelligence Generated Material” (AIGM)
• Text (§ 7681, lines 6–8):
“ ‘Artificial Intelligence Generated Material.’ An image, text or video that used artificial intelligence in whole or in part to create the image, text, simulation or video.”
• Relevance: Sets the scope for content that must be watermarked. Covers partial or full AI-created outputs.
3. “Watermark”
• Text (§ 7681, lines 9–11):
“ ‘Watermark.’ A mark placed on an artificial intelligence generated image, simulation or video.”
• Relevance: Defines the compliance mechanism (visual marking) but does not specify format beyond opacity and text.
Section B: Development & Research
— No provisions in HB 317 directly address AI research funding, data-sharing mandates, or reporting requirements. The bill focuses solely on labeling/distribution.
Section C: Deployment & Compliance
1. Watermark requirement (§ 7682, lines 12–18):
• Text:
“An individual who creates or distributes an image, text, simulation or video using artificial intelligence shall place a watermark on 30% of the image, text or video, to which the following shall apply:
(1) The watermark shall have a minimum of 50% opacity.
(2) The watermark shall contain the following statement:
‘Artificial Intelligence Generated Material.’ ”
• Impact on startups and vendors:
– Enforced on “individual[s] who create or distribute” AIGM, which arguably sweeps in independent developers, social-media users, hobbyists and commercial platforms alike.
– Technical burden: Ensuring 30% coverage at 50% opacity of every image or video may require significant tooling changes. Text watermarking for audio or text-only outputs is undefined.
• Ambiguity:
– “30% of the image, text or video” could mean area coverage for visuals, but unclear how to measure coverage on text documents or audio streams.
– No exemption for small-scale or non-commercial uses, which may chill open-source research distributions.
2. Exceptions (§ 7683, lines 20–28):
• Text:
“Film or television show productions … shall be exempt … if one of the following is met:
(1) Use of artificial intelligence for visual effects does not involve the use of an individual.
(2) If the use … involves the use of an individual, the individual has provided written consent to create a likeness of the individual.”
• Impact:
– Exempts Hollywood studios and broadcast producers when VFX don’t recreate real persons, or when talent has signed off.
– Leaves out non-film industries (marketing agencies, indie game developers).
• Ambiguity:
– “Individual” could mean any person whose likeness is simulated, leaving open what constitutes “use … of an individual.”
Section D: Enforcement & Penalties
1. Penalty for non-compliance (§ 7684, lines 1–6):
• Text:
“An individual who violates section 7682 … commits a misdemeanor of the second degree and shall be subject to a $1,000 fine for a first offense. An individual who commits a second or subsequent violation … within a five-year period … shall be subject to a $10,000 fine.”
• Impact on end-users and platforms:
– Criminal misdemeanor exposure—even for distributors unaware of AI content origins.
– Strict liability risk: no mental-state requirement (intent, knowledge) is specified.
• Enforcement by whom? The bill does not designate an enforcing agency (Attorney General, district attorneys, consumer protection division), creating uncertainty about investigatory and prosecutorial pathways.
Section E: Overall Implications
1. Restricts distribution of AI-generated media by imposing uniform, technology-specific labeling requirements.
2. Imposes compliance costs on all “individuals” creating or disseminating AIGM, potentially hampering small developers, open-source projects, and social-media sharers.
3. Provides a carve-out for high-budget film/TV under narrow conditions, favoring established studios over other creative or commercial users of generative AI.
4. Leaves open significant ambiguities—especially around coverage metrics, text/audio watermarking, and enforcement authority—that could lead to uneven or over-broad application.
5. Lacks any positive incentives or support for research, transparent AI development, or technical standards; it is purely punitive in nature.
In sum, HB 317 focuses narrowly on mandating high-visibility watermarks on AI-generated content, backed by misdemeanor penalties, while offering few clarifications or support measures. This is likely to create compliance burdens, legal uncertainty, and uneven impacts across different user communities without advancing broader AI policy goals.
House - 431 - An Act amending Title 18 (Crimes and Offenses) of the Pennsylvania Consolidated Statutes, in forgery and fraudulent practices, providing for the offense of unauthorized dissemination of artificially generated impersonation of individual.
Legislation ID: 17286
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. “Artificial intelligence.”
– Quotation: “As used in this section, the following words and phrases shall have the meanings given to them … ‘Artificial intelligence.’ Includes any of the following: (1) An artificial system that performs tasks under varying and unpredictable circumstances without significant human oversight … (5) An artificial system designed to act rationally, including an intelligent software agent or embodied robot that achieves goals using perception, planning, reasoning, learning, communicating, decision making and acting.” (Bill §4122(f), lines 19–29)
– Analysis: The bill explicitly defines “artificial intelligence” via five broad sub-definitions, targeting AI systems capable of perception, reasoning, learning or autonomous action. This catches modern machine‐learning tools, neural networks and software agents.
2. “Artificially generated impersonation.”
– Quotation: “ ‘Artificially generated impersonation.’ A visual image that appears to show or represent an individual or an auditory vocalization that appears to resemble or represent an individual’s voice that did not occur in reality and the production of which image or vocalization was substantially dependent upon technical means, including artificial intelligence or computer software…” (Bill §4122(f), lines 29–34)
– Analysis: This scope provision ties the offense to AI and other technical tools that produce synthetic images or audio. It implicitly covers deepfakes and similar AI‐driven impersonation technologies.
3. Geographic scope:
– Quotation: “A person may be convicted under this section if the victim or the offender is located within this Commonwealth.” (Bill §4122(d), lines 13–15)
– Analysis: The law applies to any dissemination event touching Pennsylvania, regardless of where the content is produced, flagging a broad territorial reach.
Section B: Development & Research
– No clauses in this bill impose obligations or restrictions on AI research, data sharing, funding mandates or reporting requirements for developers or institutions. The bill is narrowly focused on criminalizing certain uses of AI‐generated content rather than shaping R&D policy.
Section C: Deployment & Compliance
1. Prohibited conduct:
– Quotation: “A person is guilty of unauthorized dissemination of an artificially generated impersonation … if, with knowledge or reason to know or believe that the impersonation was artificially generated, the person disseminates … without the consent of the individual.” (Bill §4122(a), lines 3–7)
– Analysis: This places a compliance burden on anyone deploying AI or software to generate or distribute synthetic media. Deployers must verify consent of the person impersonated or risk criminal liability.
2. Consent defense:
– Quotation: “It is a defense … that the person disseminated the artificially generated impersonation with the consent of the individual depicted.” (Bill §4122(c), lines 10–12)
– Analysis: Startups or vendors may incorporate consent‐management features—such as digital release forms—to mitigate liability.
Section D: Enforcement & Penalties
1. Grading of offenses:
– Quotation: “An offense under subsection (a) is: (1) a misdemeanor of the first degree; or (2) a felony of the third degree, if committed with the intent to defraud or injure another person.” (Bill §4122(b), lines 7–11)
– Analysis: Penalties range from up to 5 years imprisonment for a first‐degree misdemeanor, to up to 7 years for a third‐degree felony (plus fines). This stiff penal structure may deter malicious actors but also create risk for legitimate uses.
2. Exemption for law enforcement:
– Quotation: “Nothing in this section shall be construed to apply to a law enforcement officer engaged in the performance of the law enforcement officer’s official duties.” (Bill §4122(e), lines 15–17)
– Analysis: Investigative agencies can employ synthetic impersonation (e.g., voice vaulting or image generation) without exposure to this statute, raising questions about parity between private and public sector use.
Section E: Overall Implications
– Restrictive Scope: By criminalizing the dissemination of deepfakes without consent, the bill seeks to protect individual privacy and reputations, potentially curbing malicious or deceptive uses of AI in politics, commerce, and personal affairs.
– Compliance Costs: AI developers and platforms will need processes for verifying consent, archiving proof of consent and possibly authenticating content provenance. This may favor larger vendors with compliance teams over small startups.
– Chilling Effect Concerns: Broad definitions (“visual image… that did not occur in reality”) could unintentionally capture satire, parody or artistic AI uses, stirring legal uncertainty. The “reason to know” standard may also obligate platforms to police user uploads, shifting liability and moderation costs.
– Enforcement Focus: The felony enhancement for fraudulent or injurious intent signals priority on egregious misuse, but misdemeanor liability for any non‐consensual deepfake places nearly any unpermitted distribution in criminal reach.
– No R&D Incentives: The absence of research/data sharing or innovation provisions means the bill regulates end‐use without supporting responsible AI development or public transparency initiatives.
In sum, House Bill 431 targets AI‐generated impersonation content, imposing criminal liability for non‐consensual distribution. Its clear definitions of AI and synthetic media will reshape compliance, raise moderation burdens for platforms, and likely deter malicious deepfakes—but may also ensnare legitimate creative and journalistic applications without further clarifications or carve-outs.
House - 431 - An Act amending Title 18 (Crimes and Offenses) of the Pennsylvania Consolidated Statutes, in forgery and fraudulent practices, providing for the offense of unauthorized dissemination of artificially generated impersonation of individual.
Legislation ID: 193058
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. “Artificially generated impersonation.”
– Citation: Section 4122(f), lines 3–10.
– Text: “A visual image that appears to show or represent an individual or an auditory vocalization that appears to resemble or represent an individual’s voice that did not occur in reality and the production of which image or vocalization was substantially dependent upon technical means, including artificial intelligence or computer software…”
– Analysis: This definition explicitly targets content created or manipulated by AI (“substantially dependent upon … artificial intelligence”). It is broad enough to cover deepfakes, voice-cloning, AI-generated video, and other synthetic media. The phrase “technical means” could cover future AI modalities.
2. “Artificial intelligence.”
– Citation: Section 4122(f)(1)–(5), lines 16–2 (page 2).
– Text (selected):
• “An artificial system that performs tasks … without significant human oversight or that can learn from experience and improve performance when exposed to data sets.” (f)(1)
• “An artificial system designed to think or act like a human, including cognitive architectures and neural networks.” (f)(3)
• “A set of techniques, including machine learning, that is designed to approximate a cognitive task.” (f)(4)
– Analysis: The bill adopts a multi-pronged, catch-all definition of AI. By listing machine learning, neural networks, embodied robots, and “intelligent software agents,” it includes both narrow and (potential) future general-purpose AI systems. The broad language covers existing AI developer tools (e.g., TensorFlow, PyTorch) and proprietary large-language models or vision models.
3. Territorial scope (“Applicability”).
– Citation: Section 4122(d), lines 7–10.
– Text: “A person may be convicted under this section if the victim or the offender is located within this Commonwealth.”
– Analysis: The law applies extraterritorially to any unauthorized AI-generated impersonation affecting a Pennsylvania resident or created by someone in-state. This may ensnare out-of-state AI platforms whose outputs are disseminated here.
4. Exemption for law enforcement.
– Citation: Section 4122(e), lines 10–13.
– Text: “Nothing in this section shall be construed to apply to a law enforcement officer engaged in the performance of the law enforcement officer’s official duties.”
– Analysis: AI-generated impersonations used by police (e.g., undercover operations, surveillance) are explicitly permitted, which narrows the scope of liability for state actors.
Section B: Development & Research
– The bill contains no provisions mandating or funding AI research, nor requirements for AI labs to report or share data. Its focus is solely on criminalizing certain uses of AI outputs.
– Absence note: Researchers and startups face no direct regulatory hurdles under this text, unless their tools are used to create impersonations.
Section C: Deployment & Compliance
– There are no certification, auditing, or registration requirements for AI systems.
– The only compliance obligation is an affirmative prohibition on dissemination of AI-generated impersonations without consent.
• Citation: Section 4122(a)–(b)(1), lines 11–2.
– Text: “A person is guilty … if … the person disseminates an artificially generated impersonation of an individual without the consent of the individual.”
– Impact: AI platform operators might need to implement consent-verification or take-down systems to avoid facilitating user violations.
Section D: Enforcement & Penalties
1. Mens rea and grading:
– Citation: Section 4122(a), lines 11–16; Section 4122(b)(1)–(2), lines 18–2.
• Basic offense (misdemeanor 1st degree) for unauthorized dissemination.
• Elevated offense (felony 3rd degree) “if committed with the intent to defraud or injure another person.”
– Penalties:
• Misdemeanor 1st degree under PA law: up to 5 years’ imprisonment and $10,000 fine.
• Felony 3rd degree: up to 7 years’ imprisonment and $15,000 fine.
2. Defense:
– Citation: Section 4122(c), lines 3–6.
– Text: “It is a defense … that the person disseminated the … impersonation with the consent of the individual depicted.”
– Effect: Consent must be proven by defendant. Platforms may require express opt-in for user-uploaded AI deepfakes to invoke this defense.
3. Enforcement mechanism:
– As a criminal offense, enforcement will be via district attorneys at county level. There is no private right of action.
Section E: Overall Implications
1. Restricts malicious AI deepfake use: By criminalizing nonconsensual deepfakes and voice clones, the bill aims to deter fraud, identity theft, political manipulation, harassment, and reputational harm.
2. Platform liability risk: AI content hosts and social networks may need moderation tools or user verification to avoid becoming conduits for illegal deepfakes. They face uncertain standards for “knowledge or reason to know” (Section 4122(a)).
3. Encourages consent frameworks: Legal defense is consent-based, suggesting future industry norms or certification schemes around express user consent for synthetic content.
4. Narrow research impact: Academic and corporate R&D are unaffected unless outputs are disseminated without consent; hence, safe AI experimentation remains largely unregulated.
5. Enforcement focus: Criminal penalties (instead of civil causes) signal the legislature’s intent to treat harmful deepfakes as serious offenses but leave civil remedies (defamation, privacy torts) to existing law.
Ambiguities & Future Interpretations
– “Reason to know or believe” (Section 4122(a)) is vague; will “willful blindness” or algorithmic detection obligations be required?
– “Dissemination” is undefined—does private messaging count, or only public posting?
– The breadth of “technical means” could sweep in image-editing filters or benign face-swap apps. Platforms may over‐remove content to avoid risk.
House - 518 - An Act amending the act of December 17, 1968 (P.L.1224, No.387), known as the Unfair Trade Practices and Consumer Protection Law, further providing for definitions and for unlawful acts or practices and exclusions.
Legislation ID: 17366
Bill URL: View Bill
Sponsors
House - 518 - An Act amending the act of December 17, 1968 (P.L.1224, No.387), known as the Unfair Trade Practices and Consumer Protection Law, further providing for definitions and for unlawful acts or practices and exclusions.
Legislation ID: 193145
Bill URL: View Bill
Sponsors
House - 81 - A Resolution urging the Congress of the United States to amend 17 U.S.C. §§ 102 and 107 to protect creative workers against displacement by artificial intelligence technology.
Legislation ID: 194360
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an analysis of House Resolution 81 (2025) under the requested five-part structure. Since the resolution itself is narrowly focused (it does not establish new licensing, certification or funding programs), much of the discussion centers on its proposed changes to federal copyright law—particularly how those changes single out AI systems and their use of existing works.
Section A: Definitions & Scope
1. “Artificial intelligence (AI) technology” (Whereas clause 4–6)
– Quotation: “WHEREAS, Artificial intelligence (AI) technology has advanced exponentially in recent years; … producing written works, visual art, music, computer code and more; and” (lines 4–9)
– Analysis: The resolution uses “AI technology” as an umbrella term for any system that can generate creative outputs. There is no narrower statutory definition—“AI” is left undefined, implicitly covering neural nets, large-language models, generative adversarial networks, etc.
2. “Creative workers” (Title and Whereas clause 10–13)
– Quotation: “…protect creative workers against the threat of artificial intelligence encroachment; … uncertain future for the creative sector…” (lines 10–13)
– Analysis: Although “creative workers” is not formally defined, the text refers to writers, artists, musicians, coders and the “creative sector” broadly. The resolution assumes that AI-generated output competes directly with human authorship.
Section B: Development & Research
This resolution does not directly impose R&D requirements, funding mandates or reporting obligations on AI researchers or institutions. However, by urging changes to fair-use and copyright eligibility, it has indirect impacts:
1. Restriction on “scraping” for model training
– Quotation: “(3) Explication that the feeding of copyrighted works, known as ‘scraping,’ into an AI program is not subject to the fair use doctrine, being intrinsically harmful to the market value of the copyrighted work.” (lines 11–14 in §2)
– Analysis: Labeling scraping as categorically non-fair-use would limit researchers’ ability to train large-scale models on copyrighted text, images or audio without licensing agreements. Startups and academics relying on public-domain–style arguments for research data sets would face legal uncertainty or added costs.
2. Public-domain designation for AI outputs
– Quotation: “(2) Clarification that work created in majority part by any entity other than a natural person is inherently public domain.” (lines 7–9 in §2)
– Analysis: This would discourage commercial investment in AI-originated creative R&D by providing no copyright incentive for AI-only outputs. Research labs might pivot to human-in-the-loop systems to retain IP.
Section C: Deployment & Compliance
The resolution’s proposals would reshape how AI-generated products enter the marketplace:
1. Human-authored threshold for copyright protection
– Quotation: “(1) Specification that only work created in majority part by natural persons, that is, human beings, is copyrightable.” (lines 5–7 in §2)
– Analysis: Any AI —generated art, music or code lacking a sufficient human contribution would not be protected. Vendors might embed “creative worker” disclaimers or require documented human edits to secure traditional copyright.
2. Excluding AI training from fair use
– (same as B-1)
– Analysis: Commercial platforms offering model-training services would need to negotiate licenses for text, images, music—dramatically raising costs for deployment.
Section D: Enforcement & Penalties
The resolution does not itself create new penalties or enforcement regimes; it simply urges Congress to amend existing copyright statutes:
– No new civil or criminal penalties are specified.
– Implicitly, copyright holders would gain stronger grounds for infringement suits if courts accept that (a) AI training is never fair use, and (b) AI-only outputs are public domain.
Section E: Overall Implications
1. Restrictive impact on AI ecosystem
– By removing fair use for training and stripping copyright from AI outputs, the resolution tilts the balance in favor of incumbent rights-holders (publishers, studios, record labels), raising barriers to entry for startups and academic researchers.
2. Shifting R&D strategies
– To preserve IP, AI developers may adopt stronger “human-in-the-loop” workflows, explicitly credit or compensate human contributors, or focus on licensed data sets at higher cost.
3. Potential chilling effect
– Ambiguities remain around what qualifies as “majority” human contribution. Absent clear guidelines, both researchers and end-users may avoid novel AI applications for fear of liability.
4. Policy trade-offs
– The resolution prioritizes the economic interests of existing creative workers over broader societal benefits of open AI research. It could slow innovation in generative models, while reinforcing traditional gatekeepers.
In sum, House Resolution 81 presses Congress to enact copyright amendments that would markedly restrict both the training and deployment of generative AI in order to “protect creative workers.” Although its enforcement relies on existing infringement mechanisms, its core provisions—particularly the elimination of fair-use defenses for data scraping and the denial of copyright in AI-only outputs—would reshape incentives and legal risk across the state’s and nation’s AI ecosystem.
House - 811 - An Act providing for civil liability for fraudulent misrepresentation of candidates; and imposing penalties.
Legislation ID: 193436
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused breakdown of H.B. 811 (“Fraudulent Misrepresentation of a Candidate Prevention Act”), organized into the sections you requested. Every point is tied to the bill’s own numbering and text.
Section A: Definitions & Scope
1. “Artificial intelligence” (Sec. 2, lines 13–18)
– “A machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations or decisions… including the ability to: (i) perceive… (ii) abstract perceptions… (iii) use model inference…”
– This is a broad, functional AI definition. By including “generative artificial intelligence,” the bill explicitly covers modern, large-scale models that synthesize text, image, audio or video.
2. “Generative artificial intelligence” (Sec. 2, lines 7–9)
– “The class of models that emulate the structure and characteristics of input data in order to generate derived synthetic content.”
– Targets models used to create deepfakes or other synthetic campaign material.
3. “Synthetic content” (Sec. 2, lines 15–18)
– “Information such as images, videos, audio clips and text that have been significantly modified or generated by algorithms, including artificial intelligence.”
– Any AI-altered or AI-created media falls under the bill’s prohibitions or disclosure requirements.
4. “Campaign advertisement” (Sec. 2, lines 10–14)
– “A public advertisement for the purposes of influencing public opinion… via mailings, emails, telephone calls, radio, television, billboards, yard signs or other electronic media.”
– The definition ensures that any medium—online or offline—is covered if it uses AI-generated candidate impersonations.
5. “Covered person” (Sec. 2, lines 19–30)
– Includes candidates, corporations, PACs, foreign governments, and anyone acting on their behalf (e.g., contractors).
– This broad scope means almost any AI developer, vendor or user can be held liable if they create or distribute deceptive candidate deepfakes.
Section B: Development & Research
– The bill contains no provisions mandating AI research funding, data-sharing for R&D, or reporting of AI safety tests.
– By focusing exclusively on political uses of “synthetic content,” it imposes no requirements on academic or industrial labs unless they directly disseminate campaign materials.
Section C: Deployment & Compliance
1. Civil liability for AI-driven impersonations (Sec. 3(a), lines 19–27)
– “A covered person shall be liable… if, within 90 days before an election and with willful or reckless disregard… disseminates… a campaign advertisement that contains an artificially generated impersonation of a candidate…”
– Any AI system used to generate voice mimics, face swaps or text impersonations of a candidate in the critical 90-day window is subject to liability.
2. Safe harbor via disclosure (Sec. 3(b), lines 27–30 & p. 3 lines 1–8)
– “A covered person shall not be liable… if the campaign advertisement contains a clear and conspicuous disclosure”
– Required disclosure text: “This (text/image/video/sound) has been manipulated or generated using synthetic content.”
– Display rules vary by medium (static image, video, audio)—all aimed at ensuring viewers know they’re seeing AI-generated material.
3. Exemptions (Sec. 3(h), lines 1–27)
– Traditional broadcasters, newspapers, streaming platforms, interactive computer services (47 U.S.C. § 230), ISPs, cloud providers, cybersecurity and telecom firms are shielded when merely carrying third-party ads.
– Satire or parody that relies on human impersonation rather than generative AI is also excluded.
Section D: Enforcement & Penalties
1. Private civil actions (Sec. 3(c), lines 1–12)
– “A candidate… aggrieved… may bring a civil action… and shall be entitled to recover punitive damages, reasonable attorney fees…”
– Courts can issue TROs or injunctions to remove AI-generated ads immediately.
2. Civil penalties (Sec. 3(d), lines 1–13)
– Up to $15,000 per day for municipal races; $50,000 per day for state offices; $250,000 per day for federal offices.
– PACs funding independent expenditures face double these amounts.
3. Frivolous-suit safeguards (Sec. 3(e), lines 10–21)
– If an AI-related claim is frivolous, the court may stay proceedings, require the plaintiff to show cause, dismiss the suit, and award fees to the defendant.
4. Affirmative defense (Sec. 3(f), lines 24–30)
– No liability if the candidate “has given the candidate’s express, written consent” to use the synthetic content.
5. Jurisdictional reach (Sec. 3(g), lines 1–5)
– Liability applies if either the covered person or the candidate is located in Pennsylvania, covering out-of-state AI vendors targeting Pennsylvania elections.
Section E: Overall Implications
– Business impact: AI startups and vendor platforms offering synthetic-media tools must build in compliance modes—automatic watermarking or disclosure overlays—to avoid six-figure daily fines.
– Research effect: While not directly limiting R&D, the bill signals that political-use cases for generative AI will face strict liability. Labs may self-censor or restrict public demos of candidate impersonations.
– Regulatory precedent: By defining “AI” and “generative AI” in statute and linking them to civil liability, Pennsylvania sets a template that other states could follow, potentially leading to fragmented regulation.
– Voter protection vs. free speech: The safe harbor for disclosure balances misuse prevention with legitimate AI-aided political speech, but imposing hefty penalties may chill innovative uses, especially by small campaigns or grassroots groups.
– Enforcement: Private candidate suits, rather than a state regulator, bear the burden of policing AI abuse—this could lead to uneven enforcement depending on campaign resources.
In sum, H.B. 811 is squarely targeted at AI-generated “deepfake” style attacks on candidates in the run-up to elections. Its robust definitions, liability scheme and penalties create strong disincentives for misuse, while limited exemptions and a clear-label safe harbor provide narrowly tailored compliance paths.
House - 95 - An Act amending the act of December 17, 1968 (P.L.1224, No.387), known as the Unfair Trade Practices and Consumer Protection Law, further providing for definitions.
Legislation ID: 192762
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused breakdown of PA HB 95 (Printer’s No. 78), organized as requested. All quotations refer to the bill’s internal numbering (for example “Section 2(4)(xx.1)” means page 2, clause 4, subclause xx.1).
Section A: Definitions & Scope
1. “Artificial intelligence”
• Quotation: “(14) ‘Artificial intelligence’ means technology or tools that use predictive algorithms to create new content, including audio, code, images, text, simulations or videos.” (Sec. 2(14), lines 11–13)
• Analysis: This is the bill’s principal AI definition. It explicitly covers “predictive algorithms” that “create new content,” thereby targeting generative AI systems (e.g. large language models, image generators, voice‐cloning tools). Any tool that meets this definition falls under the new disclosure requirement.
2. “Clear and conspicuous”
• Quotation: “(15) ‘Clear and conspicuous’ means a statement or disclosure that meets all of the following criteria…” (Sec. 2(15), lines 14–30)
• Analysis: This multi-part definition lays out technical requirements (size, color, duration, proximity) for disclosure. By codifying what “clear and conspicuous” disclosure entails, the bill ensures that AI-generated content cannot be hidden in fine print or obscured.
Section B: Development & Research
• No clauses in this bill impose direct mandates on AI R&D, data sharing, or research funding. The bill’s scope is limited to consumer protection and does not include provisions for grants, public-sector AI projects, or university research protocols.
Section C: Deployment & Compliance
1. Disclosure Requirement for AI-Generated Content
• Quotation: “(xx.1) Knowingly or recklessly creating, distributing or publishing any content generated by artificial intelligence without clear and conspicuous disclosure…” (Sec. 2(4)(xx.1), lines 1–4)
• Analysis: This is the core regulatory provision. Any person or entity that “knowingly or recklessly” presents AI content to consumers must label it as such. It covers “written text, images, audio and video content and other forms of media.”
• Impact on Startups & Vendors: Companies offering generative AI tools would need to build in labeling features—e.g. watermarking generated images or prepending text outputs with “Generated by AI.”
• Impact on End-Users & Platforms: Publishers, social networks, news outlets or any website embedding AI content will need to ensure disclosures are “in the first instance when the content is presented” and “in the same medium.” Failure to comply risks enforcement under the Unfair Trade Practices law.
Section D: Enforcement & Penalties
• The bill amends the “Unfair Trade Practices and Consumer Protection Law,” empowering the Attorney General and District Attorneys to enforce it (see title and Section 1).
• While it does not specify new dollar-amount fines, it makes undisclosed AI content an “unfair or deceptive act or practice,” subject to existing civil penalties (injunctions, restitution, civil fines up to $10,000 per violation under 73 P.S. § 201-9.3).
• Ambiguity: The phrase “knowingly or recklessly” could be interpreted as requiring proof of intent or gross negligence; ordinary negligence may not trigger liability.
Section E: Overall Implications
1. Increased Transparency
– By mandating clear labeling, the bill aims to reduce consumer confusion about whether content is human- or machine-generated.
2. Compliance Burden
– Generative AI providers, media companies, advertisers and platform operators will need to audit their content pipelines, implement detection or watermarking, and design user interfaces that surface disclosures in compliance with the “clear and conspicuous” standard.
3. Innovation vs. Restriction
– The rule does not ban AI generation, nor does it impose technical standards on the AI models themselves. Its focus on disclosures is unlikely to stifle core R&D, but it could create friction for small vendors who lack resources to augment their models with disclosure layers.
4. Enforcement Risk
– Because violations are treated under the state’s consumer protection law, even non-commercial authors (e.g. bloggers) could theoretically face action if they publish AI content without labeling it. The risk of private class actions under Pennsylvania’s “unfair practices” statute may also chill unlabeled AI usage.
In sum, PA HB 95 introduces a narrowly tailored transparency requirement for generative AI outputs, backed by consumer-protection enforcement. It does not impose direct R&D mandates or technical quality controls on AI systems, but it does necessitate procedural and UI changes for any entity distributing AI‐created media.
Senate - 293 - An Act providing for a report on artificial intelligence in the workforce; and imposing duties on the Department of Labor and Industry and Department of Community and Economic Development.
Legislation ID: 194811
Bill URL: View Bill
Sponsors
Senate - 431 - An Act amending the act of February 14, 2008 (P.L.6, No.3), known as the Right-to-Know Law, in preliminary provisions, further providing for definitions; and, in procedure, providing for acceptable denials.
Legislation ID: 194938
Bill URL: View Bill
Sponsors
Senate - 431 - An Act amending the act of February 14, 2008 (P.L.6, No.3), known as the Right-to-Know Law, in preliminary provisions, further providing for definitions; and, in procedure, providing for acceptable denials.Co-Sponsorship MemoCombating Cybersecurity Risks and Artificial Intelligence Right-to-Know Requests- opens in a new tab
Legislation ID: 114294
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a structured analysis of Senate Bill 431 (PN 465), with every point tied to the text you provided.
Section A: Definitions & Scope
1. “Artificial intelligence.”
– Text (Sec. 102, new definition, lines 1–9):
“‘Artificial intelligence.’ A machine-based system that can, for a given set of explicit or implicit objectives, make predictions, content, recommendations or decisions influencing real or virtual environments, including the ability to: (1) perceive real and virtual environments; (2) abstract perceptions made under paragraph (1) into models through analysis in an automated manner; and (3) use model inference to formulate options for information or action based on outcomes under paragraphs (1) and (2).”
– Analysis: This is a broad, functional definition. It explicitly covers systems that “perceive,” “abstract,” and “use model inference,” thereby targeting classic AI subsystems (e.g., computer vision, machine learning).
2. “Generative artificial intelligence.”
– Text (Sec. 102, lines 11–16):
“‘Generative artificial intelligence.’ The class of models that emulate the structure and characteristics of input data in order to generate derived synthetic content, including information such as images, videos, audio clips or text that has been significantly modified or generated by algorithms, including by artificial intelligence.”
– Analysis: By singling out “generative” systems, the bill signals special scrutiny or treatment for AI that produces new content (e.g., GPT-style models, deepfakes).
Section B: Development & Research
– There are no direct mandates, funding requirements, reporting obligations, or data-sharing rules targeting AI R&D in this bill. The amendments focus on how public-records requests that relate to AI may be handled procedurally.
Section C: Deployment & Compliance
– This bill does not impose certifications, audits, or substantive operational restrictions on AI systems in deployment. Its only operational impact is to allow agencies to refuse public-records requests that appear to be generated or submitted by AI. See Section 709 below.
Section D: Enforcement & Penalties
1. Acceptable Denials for AI-Generated Requests
– Text (Sec. 709(a)(2), lines 29–30 through next page lines 1–2):
“(2) The agency has reasonable suspicion that the request was automatically generated by a computer program, script, artificial intelligence or generative artificial intelligence.”
– Analysis: Agencies can now lawfully reject any public-records request if they “reasonably suspect” it was created by AI. There is no further definition of “reasonable suspicion,” which creates ambiguity:
• Could a simple IP-address check suffice?
• Must agencies document their suspicion?
2. Cybersecurity-Based Denial
– Text (Sec. 709(a)(1), lines 21–27):
“(1) The agency’s open-records officer or information technology professional reasonably believes that downloading attached documents or accessing hyperlinks within the request could pose a cybersecurity risk to the agency’s network. This paragraph shall not be construed to conflict with an agency receiving or accepting a request under section 505(a).”
– Analysis: While this clause is not AI-specific, it interacts with AI because many AI-driven phishing or supply-chain attacks use malicious attachments or links. It effectively allows agencies to treat AI-originated requests more skeptically under established cybersecurity exceptions.
3. Appeal Right
– Text (Sec. 709(b), lines 3–5):
“A requester may appeal a denial issued under subsection (a) as provided under Chapter 11.”
– Analysis: This grants a procedural remedy. However, it does not limit grounds on which an appeal can succeed, nor does it require agencies to detail their “reasonable suspicion.”
No monetary penalties or criminal sanctions are added; the only “enforcement” mechanism is the ordinary appeal process in the Right-to-Know Law (Chapter 11).
Section E: Overall Implications
1. Chilling Effect on AI-Driven Transparency Requests
By explicitly allowing blanket denials of requests “automatically generated” by AI, the bill may deter the use of AI tools by journalists, watchdog groups, and researchers who rely on Right-to-Know requests. Agencies could broadly interpret “reasonable suspicion” to reject requests en masse.
2. Increased Agency Discretion
The undefined standard of “reasonable suspicion” grants open-records officers wide latitude. Without clear proof requirements, decisions may become subjective, inconsistent, and potentially shield agencies from scrutiny.
3. Cybersecurity Pretext
Although framed as a cybersecurity safeguard, denying AI-originated requests could be used as a pretext to reject valid requests that merely arrive with automated formatting or hyperlinks.
4. No Positive AI Governance
The bill does not promote best practices, transparency standards, or accountability for AI systems themselves—only procedural controls over how agencies handle incoming requests.
5. Potential for Legal Challenge
Because the Right-to-Know Law’s purpose is broad public access (42 Pa.C.S. §§ 6501 et seq.), affected requesters may challenge overbroad or bad-faith denials, arguing that “reasonable suspicion” cannot override statutory access goals without specific legislative standards.
In sum, SB 431’s AI-related provisions are narrowly procedural—defining “AI” and “generative AI” and empowering agencies to refuse records requests they believe come from such systems. It does not otherwise regulate AI development, deployment, or safety, but it could meaningfully hinder AI-augmented freedom-of-information usage in Pennsylvania.
Senate - 806 - An Act amending the act of December 17, 1968 (P.L.1224, No.387), known as the Unfair Trade Practices and Consumer Protection Law, further providing for definitions.
Legislation ID: 195298
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. “Artificial intelligence”
• Citation: Section 2, clause (14) (lines 10–13).
• Text: “(14) ‘Artificial intelligence’ means technology or tools that use predictive algorithms to create new content, including audio, code, images, text, simulations or videos.”
• Analysis: This clause explicitly targets all AI systems that generate novel media via predictive models. It is broad enough to cover large language models, image generators, deep-fake makers, text-to-speech engines, code-generation AIs, and similar tools.
2. “Unfair or deceptive acts or practices” extended to AI content without disclosure
• Citation: Section 2(4)(xx.1) (lines 18–25).
• Text: “(xx.1) Knowingly or recklessly creating, distributing or publishing any content generated by artificial intelligence without clear and conspicuous disclosure….”
• Analysis: This provision makes undisclosed AI-generated content a per se unfair trade practice. It applies to any media type (text, images, audio, video, other forms) when “knowingly or recklessly” undislosed, effectively mandating labels on AI outputs.
3. “Clear and conspicuous”
• Citation: Section 2, clause (15) (lines 13–30).
• Text: “(15) ‘Clear and conspicuous’ means a statement or disclosure that meets all of the following criteria….”
• Analysis: Defines how AI-content labels must appear—size, color, contrast, proximity, duration for video/audio—to ensure consumer notice.
Section B: Development & Research
No provisions in this bill address AI R&D funding, data sharing, or research reporting requirements. The focus is exclusively on downstream labeling of AI-generated content in commercial settings.
Section C: Deployment & Compliance
1. Mandatory Disclosure for AI Content
• Citation: Section 2(4)(xx.1) (lines 18–25).
• Impact: Companies deploying AI for content creation—advertisers, media outlets, social platforms—must add prominent AI labels at first presentation. They risk being found in violation of consumer-protection law if they “knowingly or recklessly” omit the disclosure.
2. Compliance Specifications
• Citation: Section 2, clause (15)(i–vi) (lines 14–30).
• Impact: Deployment teams must adapt UIs, print layouts, audio tracks, video overlays to meet “clear and conspicuous” criteria. This creates design and operational overhead for compliance.
Section D: Enforcement & Penalties
1. Enforcement Mechanism
• Citation: Title references to the Unfair Trade Practices and Consumer Protection Law (P.L.1224, No.387).
• Impact: The Pennsylvania Attorney General and District Attorneys can pursue civil actions, injunctions, and penalties against businesses that publish AI content without required disclosures, under existing remedies for unfair or deceptive trade practices.
2. Penalties
• Not explicitly restated in this amendment.
• Underlying law: Penalties include civil fines (up to $10,000 per violation), restitution, equitable relief.
Section E: Overall Implications
1. Greater Transparency Mandate
• This bill represents a regulatory first in Pennsylvania, mandating that AI-generated content be clearly labeled. It aims to combat misinformation and “deep-fake” risks by educating end users.
2. Compliance Burden on Businesses
• Startups and established vendors must build labeling mechanisms for every AI output channel—web, print, audio, video—to avoid legal risk. This could increase costs and slow product releases.
3. Little Direct Impact on R&D
• Absence of R&D incentives or restrictions means Pennsylvania remains neutral on AI model training or data-use practices, focusing solely on consumer-facing transparency.
4. Ambiguities
• “Knowingly or recklessly” standard: Unclear threshold for publishers—does inadvertent omission qualify?
• “Other forms of media”: Undefined scope—interactive chatbots? Virtual or augmented reality? These ambiguities could lead to enforcement challenges.
In sum, SB 806 adds a stringent labeling requirement for AI content under Pennsylvania’s consumer-protection framework, reshaping how AI-powered media is deployed and regulated in the state without affecting upstream development or research.
Senate - 939 - An Act providing for high impact data centers; establishing the Office of Transformation and Opportunity and the Artificial Intelligence, Data Center and Emerging Technology Regulatory Sandbox Program; and providing for powers and duties of office.
Legislation ID: 215558
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of Senate Bill 939 (2025), organized per your requested structure. All quotations cite section and line numbers from the bill.
Section A: Definitions & Scope
1. “Artificial intelligence.”
• Text: “Artificial intelligence. Technology or tools that use predictive algorithms to create new content, including audio, code, images, text simulations or videos.” (Sec. 102, lines 9–12)
• Analysis: This definition explicitly anchors the act’s AI scope on “predictive algorithms” and “new content.” By focusing on content-generating AI, the bill frames its sandbox to include generative AI systems (e.g., text or image models) but may exclude narrower AI uses (e.g., predictive analytics without content creation).
2. “Emerging technology.”
• Text: “Emerging technology. Technologies still in development and deemed by the office to have a significant impact on society in the next decade, including, but not limited to, artificial intelligence, robotics and blockchain technology.” (Sec. 102, lines 24–27)
• Analysis: This catch-all provision empowers the Office to bring other future technologies under the sandbox umbrella. Its broad phrasing (“deemed by the office”) could be interpreted flexibly to admit new AI paradigms (e.g., foundation models, biotechnologies).
3. “Innovative artificial intelligence, data center and emerging technology product or service.”
• Text: “An artificial intelligence, data center and emerging technology product or service that includes an innovation.” (Sec. 501, lines 20–23)
• Analysis: The act ties AI services to “innovation,” requiring them to be novel or newly applied. This both focuses the sandbox on cutting-edge AI and excludes routine IT operations.
Section B: Development & Research
1. Regulatory Sandbox Established (Sec. 502(a), lines 5–13)
• Text: “The Artificial Intelligence, Data Center and Emerging Technology Regulatory Sandbox Program is established in the office.… The program shall enable a person to obtain limited access to the market in this Commonwealth to test an innovative… product or service without obtaining a license or other authorization that might otherwise be required.”
• Impact: By waiving normal licensing barriers, the sandbox accelerates AI prototyping and pilot deployments in‐state. Researchers and startups can test novel generative models or AI‐driven services before securing full regulatory approval.
2. Application Requirements (Sec. 502(c), lines 23–49)
• Text (excerpt): “(5) Demonstrates that the applicant has… technical expertise… and developed plan to test… an innovative artificial intelligence… product or service. (6) Contains a description of the… product or service to be tested, including… (i) how the… product… is subject to licensing… outside of the program.”
• Impact: The bill compels applicants to document R&D capacity, risk assessments, and existing licensing hurdles. This may steer participants toward robust testing plans but could burden small research teams with extensive paperwork.
3. Reporting and Recordkeeping (Sec. 511(a)–(c), lines 1–18)
• Text: “A participant shall retain records, documents and data produced in the ordinary course of business regarding an… product or service tested… If a… product or service… fails… the participant shall notify the office and report on actions taken… The office shall establish quarterly reporting requirements… including… customer complaints.”
• Impact: Quarterly data collection on performance and failures fosters empirical insights into AI safety and efficacy. Regulators gain structured visibility into emerging AI risks, but participants may find recordkeeping onerous, especially for rapidly evolving prototypes.
Section C: Deployment & Compliance
1. Licensing Waivers (Sec. 504(d), lines 3–10)
• Text: “(1) A participant is deemed to possess an appropriate license… for the purposes of any provision of Federal law requiring State licensure… (2) A participant… is not subject to the requirements… that were identified by the participants application and have been waived in writing by the office.”
• Impact: Federal compliance (e.g., for fintech AI services) becomes streamlined if the sandbox deems participants licensed. However, ambiguity may arise: participants might misunderstand which specific regulations remain in force.
2. Consumer Disclosures (Sec. 508(a), lines 3–20)
• Text (excerpt): “Before providing an… product or service… a participant shall disclose… (3) that the… product or service is undergoing testing and may not function as intended and may expose the customer to financial risk. (4) that the provider… is not immune from civil liability… (6) the expected end date of the testing period.”
• Impact: Mandatory disclosures protect early adopters by clarifying that AI outputs are provisional. This transparency may build consumer trust but could deter uptake if warnings appear too prominent or legalistic.
3. Exiting the Program (Sec. 509(a), lines 14–24)
• Text: “At least 30 days before the end of the testing period, a participant must… notify the office that it will exit the program… or seek an extension…”
• Impact: This exit protocol helps regulators ensure AI pilots end cleanly or transition to full licensing. Startups must plan licensing applications well in advance, possibly diverting resources from product development.
Section D: Enforcement & Penalties
1. Termination of Participation (Sec. 506, lines 1–6)
• Text: “By written notice, the office may terminate a participants participation… at any time and for any reason, including if the applicable department determines the participant is not operating in good faith…”
• Impact: Broad termination authority lets regulators swiftly end pilots that pose safety or consumer‐protection concerns. The lack of detailed standards for “good faith” may generate legal uncertainty for participants.
2. Civil Liability (Sec. 505, lines 1–3)
• Text: “A participant does not have immunity related to any criminal offense committed during participation in the program.”
• Impact: Participants remain fully accountable for criminal misconduct (e.g., data misuse, fraud). No blanket liability shield encourages compliance but may deter risk-averse entities from participating.
3. Removal for Law Violations (Sec. 511(c)(3), lines 12–18)
• Text: “If the office determines that a participant… is about to engage in any practice… that constitutes a violation of Federal or State criminal law, the office may remove the participant from the program.”
• Impact: This preventive removal power further safeguards consumers, though the undefined threshold for “about to engage” leaves room for debate over when removal is appropriate.
Section E: Overall Implications
• Accelerates Innovation: The sandbox’s licensing waivers (Sec. 504(d)) and “deemed licensing” for Federal purposes lower entry barriers for AI startups and labs, encouraging in-state experimentation with generative models, robotics, or blockchain integrations.
• Consumer Protections: Detailed disclosure requirements (Sec. 508) and rapid termination/removal powers (Secs. 506; 511(c)(3)) balance innovation with risk mitigation, giving end-users clear notice and regulators tools to intervene.
• Regulatory Clarity vs. Ambiguity: While the act mandates consultation with “applicable agencies” (Sec. 503(a)(1)) to map out which rules are waived, it does not list them exhaustively. This could lead to uncertainty about which specific licensing pathways remain in effect post-sandbox.
• R&D Oversight: Quarterly reporting (Sec. 511) provides the state with rich data on emerging AI risks and consumer responses, potentially informing future AI policy. Yet, the administrative burden may be heavy for small teams without dedicated compliance staff.
• Flexibility for New AI Trends: The “deemed by the office” language in the “emerging technology” definition (Sec. 102) lets the Commonwealth quickly adapt the sandbox to novel AI developments—be they neuromorphic computing, AI-driven biotech, or quantum-enhanced ML—without needing immediate statutory amendments.
In sum, SB 939 establishes a permissive yet monitored environment for piloting generative and other AI innovations, backed by consumer-protection disclosures and swift enforcement tools. Its success will hinge on clear agency guidance about which regulations are waived, balanced against the administrative load placed on AI researchers and startups.
Texas
House - 1121 - Relating to civil and criminal liability for the unlawful disclosure or promotion of intimate visual material.
Legislation ID: 130562
Bill URL: View Bill
Sponsors
House - 1265 - Relating to artificial intelligence mental health services.
Legislation ID: 19900
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI‐focused analysis of H.B. A1265 (proposed Ch. 616, Health & Safety Code), organized per your requested outline. All quotations cite section and subdivision exactly as in the bill text.
Section A – Definitions & Scope
1. “Artificial intelligence” (Sec. A616.001(1))
• Relevance to AI: this definition (“computer software designed to simulate human intelligence through machine learning and perform tasks normally requiring human involvement”) explicitly targets any software that applies ML models or related techniques to mimic human reasoning—i.e., generic AI systems.
• Potential ambiguity: “simulate human intelligence” and “machine learning” are broad; could capture both rule-based expert systems and modern neural nets. Startups may need clarity on whether simpler decision trees qualify.
2. “Artificial intelligence mental health services” (Sec. A616.001(2))
• Relevance to AI: defines the regulated activity—“counseling, therapy, or other mental health services provided through the use of artificial intelligence.”
• Scope: encompasses any automated or semi-automated therapeutic chatbots, virtual therapists, or AI-augmented diagnostic tools.
3. “Commission” and “Executive commissioner” (Sec. A616.001(3)–(4))
• Relevance: identifies the Health and Human Services Commission and its executive commissioner as the regulatory authority for AI mental health.
4. “Licensed mental health professional” (Sec. A616.001(5))
• Scope: clarifies that AI mental-health services must be overseen by state-licensed practitioners (psychiatrists, psychologists, etc.).
Section B – Development & Research
There are no provisions mandating AI R&D funding, data sharing, or public–private research partnerships. The sole “testing” regime for AI apps (Sec. A616.003) is developer-driven, not state-supported research.
Section C – Deployment & Compliance
1. Approval requirement (Sec. A616.002(a)(1))
• “the artificial intelligence application through which the services are provided is commission-approved under Section 616.003”
• Impact: Every vendor must submit testing results and obtain explicit HHSC approval before offering AI mental-health tools in Texas. This creates a gatekeeping step likely to slow time-to-market, raising compliance costs for startups and established vendors alike.
2. Licensed-professional oversight (Sec. A616.002(a)(2))
• “the person providing the services is a licensed mental health professional or a person who ensures a licensed mental health professional is available at all times.”
• Impact: AI vendors cannot operate standalone—must embed live licensure. In practice, small innovators may struggle to contract 24/7 licensed backup.
3. Professional intervention triggers (Sec. A616.002(b)(3))
• “intervene… if the individual is: (A) threatening harm to self or others; or (B) reporting abuse or neglect of a child.”
• Impact: imposes real-time human safety nets. Although important for client safety, this could limit use of AI in crisis detection without robust human staffing.
4. Informed consent & disclosure (Sec. A616.002(c))
• “clearly advise each individual… that the services are provided through artificial intelligence; and … obtain the individual’s informed consent”
• Impact: adds transparency requirements that protect users but require additional UI/UX design work and legal review for all digital AI mental-health products.
5. Testing regime (Sec. A616.003(a)–(c))
• (a) permits only “testing” on volunteers with full liability releases.
• (b) “only… considered to have successfully completed testing after … demonstrate competency and safety.”
• (c) “the commission shall evaluate … testing results and issue an order approving or disapproving.”
• Impact: sets up a phased deployment: first, limited trials (with high‐risk waiver of liability), then full approval. This “pre-market” review is akin to a medical device pathway—potentially slowing innovation but aiming to ensure safety.
6. Ethics & nondiscrimination (Sec. A616.004(a)–(b))
• (a) licensed professionals “guided by the ethical principles and standards applicable… without the use of artificial intelligence.”
• (b) “may not discriminate … on the basis of race, ethnicity, gender, sexual orientation, or any other characteristic.”
• Impact: requires AI tools meet existing professional codes (e.g., APA ethics) and explicit fairness standards. Vendors must audit models for bias and document compliance.
Section D – Enforcement & Penalties
1. Regulatory recognition & disciplinary action (Sec. A616.005)
• (a) “Each state agency… shall recognize as authorized … AI mental health services … approved under this chapter.”
• (b) “A person… who violates… a professional licensing statute is subject to disciplinary action … regardless of whether the person is licensed.”
• Impact: places AI-service providers under the same disciplinary umbrellas as human practitioners—state boards can suspend or revoke AI-service privileges if misuse occurs.
2. Reporting and recordkeeping (Sec. A616.006)
• “shall maintain records … in the same manner as required by the applicable professional licensing statute.”
• Impact: demands that AI interactions be logged and auditable to the same extent as human-delivered care, which may raise data-storage and privacy burdens.
3. Rulemaking authority (Sec. A616.007)
• “The executive commissioner shall adopt rules necessary to implement this chapter.”
• Impact: leaves key definitions, timelines, fees, application forms, and enforcement details to future rulemaking—introducing uncertainty until rules are in place.
Section E – Overall Implications
• Innovation pace vs. patient safety: By imposing a medical-device-like approval process, mandatory licensed oversight, and stringent record-keeping, the bill prioritizes safety and professional standards but likely slows entry of new AI mental-health tools.
• Market barriers: Startups and small vendors face substantive compliance costs—testing protocols, legal drafting of waivers, staff of licensed professionals, application fees, and potential delays in commission review.
• Established vendors: Larger companies with regulatory experience (e.g., telehealth giants) may be better positioned to absorb costs and navigate HHSC approval.
• Researchers: With no state-funded R&D incentives or data-sharing mandates, academic and private research remains largely unaffected except insofar as commercial trials in Texas require the commission’s approval.
• End-users: Patients gain greater transparency, professional safeguards, and nondiscrimination guarantees but may see reduced availability of novel AI services locally.
In sum, H.B. A1265 creates a clear statutory framework for safe, licensed, and monitored deployment of AI in mental health care but at the cost of higher regulatory and operational overhead. This reshapes Texas’s AI ecosystem by embedding healthcare AI firmly within existing professional structures rather than allowing stand-alone technology deployments.
House - 149 - Relating to regulation of the use of artificial intelligence systems in this state; providing civil penalties.
Legislation ID: 130073
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. “Artificial intelligence system” (Sec. 551.001(1))
– Definition: “any machine-based system that, for any explicit or implicit objective, infers from the inputs the system receives how to generate outputs…”
– Relevance: This catch-all definition triggers all duties, prohibitions, and enforcement in Chapters 551–554.
2. “Developer” and “Deployer” (Sec. 552.001(1)–(2))
– Developer: “a person who develops an artificial intelligence system that is offered, sold, leased, given, or otherwise provided in this state.”
– Deployer: “a person who deploys an artificial intelligence system for use in this state.”
– Relevance: Creates two regulated roles in the AI lifecycle—those who build models and those who put them into operation.
3. Applicability (Sec. 551.002)
– Applies to any person who “promotes, advertises, or conducts business in this state” or “develops or deploys an AI system in this state.”
– Relevance: Broad geographic and substantive reach.
Section B: Development & Research
1. Biometric data in training (Sec. 503.001(e)(2))
– Exemption for “training, processing, or storage of biometric identifiers involved in developing, training…of AI models or systems, unless a system is used…for the purpose of uniquely identifying a specific individual.”
– Impact: Encourages R&D that uses biometric data, with a carve-out for identity systems. Startups can train on biometrics so long as they do not deploy them for face/iris ID.
2. Data-protection assessment (Sec. 541.104(a)(2))
– Processors must assist controllers “relating to the security of processing personal data collected, stored, and processed by an AI system…”
– Impact: Imposes R&D overhead—private research labs must build data security controls and subject proofs to outside controllers.
3. Regulatory sandbox (Chapter 553)
– Sec. 553.051(a): “The department…shall create a regulatory sandbox…to test innovative AI systems without obtaining a license, registration, or other regulatory authorization.”
– Sec. 553.051(c–d): No punitive action during testing period for waived laws.
– Impact: Significantly advances experimentation, lowers barriers for startups and researchers to pilot AI innovations. 36-month term (Sec. 553.053(a)). Quarterly reporting required (Sec. 553.102).
Section C: Deployment & Compliance
1. Consumer disclosure (Sec. 552.051(b))
– “A governmental agency…shall disclose to each consumer…that the consumer is interacting with an AI system.”
– Impact: Agencies must label AI chatbots or decision tools. Sets precedent; may bleed into private sector.
2. Prohibition on behavioral manipulation (Sec. 552.052)
– Bans deployment “in a manner that intentionally aims to incite or encourage a person to…commit self-harm,…harm another, or engage in criminal activity.”
– Impact: Developers must embed guardrails preventing harmful prompts or content; affects generative AI safety layers.
3. Social scoring ban (Sec. 552.053)
– “A governmental entity may not use or deploy…AI…with the intent to calculate or assign a social score…that results in detrimental or unfavorable treatment…”
– Impact: Prevents government from deploying risk-assessment or credit-style AI for citizens. Encourages transparent use.
4. Biometric identification ban (Sec. 552.054(b))
– “A governmental entity may not…use AI…for the purpose of uniquely identifying a specific individual using biometric data…without the individuals consent…”
– Impact: Halts government face recognition deployment absent consent. Private sector sections referenced in Sec. 503.001 also apply.
5. Unlawful discrimination (Sec. 552.056(b))
– “A person may not…deploy an AI system with the intent to unlawfully discriminate against a protected class in violation of state or federal law.”
– Impact: Vendors must test for bias; legal risk if model decisions disparately impact protected groups. Intent requirement makes enforcement harder when intent is inferred.
Section D: Enforcement & Penalties
1. Civil penalty structure (Sec. 552.105(a))
– “For each curable violation…$10,000–$12,000; for each uncurable…$80,000–$200,000; for continued violation…$2,000–$40,000 per day.”
– Impact: High fines deter noncompliance. “Cure period” of 60 days (Sec. 552.104).
2. Attorney General’s authority (Sec. 552.101)
– “The attorney general has exclusive authority to enforce this chapter…no private right of action.”
– Impact: Centralizes enforcement; reduces litigation risk from private suits but increases dependency on AG’s agenda.
3. Defense safe harbors (Sec. 552.105(e))
– No liability if violation discovered through “testing, including adversarial or red-team testing” or compliance with NIST AI Risk Management Framework.
– Impact: Incentivizes best practices, third-party audits, and adversarial testing regimes by developers.
Section E: Overall Implications
– The Act establishes one of the nation’s most comprehensive state-level AI regulatory regimes, covering definitions, R&D exemptions, deployment rules, consumer disclosures, fairness, privacy, and a regulatory sandbox.
– Research & Innovation: The biometric exemption in Sec. 503.001(e)(2) and the sandbox (Ch. 553) foster experimentation, but data security and reporting burdens may slow small players.
– Commercial Deployment: Strict bans on manipulation, social scoring, and biometric ID curtail certain use-cases (e.g., government surveillance, credit scoring), steering AI providers toward more transparent and consent-based models.
– Compliance Overhead: Labels, data protection assistance, assessments, and quarterly sandbox reporting will increase operational costs for providers and prompt robust compliance programs.
– Enforcement: Heavy penalties balanced by “cure” and safe-harbor provisions encourage good-faith remediation and alignment with NIST guidelines. Exclusive AG enforcement centralizes oversight but may limit public recourse.
House - 1709 - Relating to the regulation and reporting on the use of artificial intelligence systems by certain business entities and state agencies; providing civil penalties.
Legislation ID: 20301
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of H.B. ANo. A1709 (“Texas Responsible Artificial Intelligence Governance Act”), organized into the five sections you requested. All claims are anchored to quoted text from the bill.
Section A: Definitions & Scope
1. “Artificial intelligence system” (Sec. A551.001(2))
• Quote: “Artificial intelligence system means the use of machine learning and related technologies that use data to train statistical models for the purpose of enabling computer systems to perform tasks normally associated with human intelligence or perception….”
• Relevance: This is the Act’s core definition of “AI,” covering both traditional ML and generative tasks (vision, NLP, content generation). It ensures nearly any software employing statistical learning falls under the Act.
2. “High-risk artificial intelligence system” (Sec. A551.001(14)–(15))
• Quote: “‘High-risk artificial intelligence system’ means any artificial intelligence system that is a substantial factor to a consequential decision.”
• Relevance: The Act draws a bright line between “high-risk” AI (subject to strict duties) and other AI (mostly exempt), hinging on whether it influences “consequential decisions.”
3. “Consequential decision” (Sec. A551.001(5))
• Quote: “Consequential decision means any decision that has a material, legal, or similarly significant, effect on a consumer’s access to… employment… housing… elections or voting process.”
• Relevance: The Act’s compliance regime only triggers for AI that materially affects rights or services protected by law.
4. “Generative artificial intelligence” (Sec. A551.001(13)) and “Open source artificial intelligence system” (Sec. A551.001(15))
• Relevance: By defining these, the Act separately addresses large‐scale generative models and grants special (lighter) treatment to open-source systems that come with full transparency.
5. “Developer,” “Distributor,” “Deployer” (Sec. A551.001(8)–(9),(12))
• Relevance: The Act applies distinct duties at each stage: developers (create or substantially modify AI), distributors (bring it to market), deployers (operate it in Texas).
6. Applicability (Sec. A551.002)
• Quote: “This chapter applies only to a person that is not a small business… and (1) conducts business… in this state; or (2) engages in the development, distribution, or deployment of a high-risk AI system in this state.”
• Relevance: Small businesses (per SBA definitions) are exempt, focusing enforcement on larger actors.
Section B: Development & Research
1. Developer obligations (Sec. A551.003)
• “(a) A developer of a high-risk AI system shall use reasonable care to protect consumers from any known or reasonably foreseeable risks of algorithmic discrimination….”
• “(b) …Prior to providing a high-risk AI system to a deployer, a developer shall provide … a High-Risk Report that consists of [intended uses, known limitations, bias-mitigation measures, data governance, etc.].”
• Impact: Imposes a detailed pre-deployment reporting requirement on creators of high-risk AI, driving early risk-assessment activity. Researchers and startups will need to codify test metrics, bias-tests, and documentation before any commercialization.
2. Duty to correct non-compliance (Sec. A551.003(d)–(e))
• Quote: “If a developer believes … that it deployed a high-risk AI system that does not comply… the developer shall immediately take the necessary corrective actions… withdraw it, disable it, and recall it, as appropriate… and inform the attorney general….”
• Impact: Creates legal incentives for ongoing monitoring and rapid response—a resource burden on small R&D shops.
3. Recordkeeping for generative AI (Sec. A551.003(f))
• Quote: “Developers shall keep detailed records of any generative AI training data used… consistent with … the NIST ‘AI Risk Management Framework: Generative AI Profile.’”
• Impact: Forces adoption of NIST’s guidance in recordkeeping, aligning state policy with federal best practices.
4. Sandbox exception (Sec. A551.012)
• Quote: “Excluding violations of Subchapter B, this chapter does not apply to … research, training, testing … performed by active participants of the sandbox program in compliance with Chapter 552.”
• Impact: Encourages experimentation under controlled conditions, waiving developer/deployer duties so long as participants comply with sandbox rules.
Section C: Deployment & Compliance
1. Impact Assessments (Sec. A551.006)
• Quote: “A deployer … shall complete an impact assessment … annually and within ninety days after any intentional and substantial modification… must include: (1) purpose…; (2) analysis of known or reasonably foreseeable risks of algorithmic discrimination…; (3) …data inputs and outputs…; (6) transparency measures…; (8) cybersecurity measures….”
• Impact: Mirrors GDPR-style DPIAs, requiring every high-risk AI operator to document and update a broad risk profile, transparency, and security posture—raising compliance costs, especially for startups and mid-size firms.
2. Disclosure to consumers (Sec. A551.007)
• Quote: “A deployer or developer … shall disclose to each consumer, before or at the time of interaction: (1) that the consumer is interacting with an AI system; (2) the purpose; (3) that the system may… make a consequential decision; (4) nature of the decision; (5) factors used…; (6) contact information…; (7) human vs. automated components…; (8) consumer rights under Sec. 551.108.”
• Impact: Ensures end-users are fully informed, boosting transparency but requiring UI/UX changes and possible legal disclaimers. Ambiguity: “contact information of the deployer” could range from a support email to a regulated legal-entity address.
3. Risk Management Policy (Sec. A551.008)
• Quote: “A developer or deployer … shall, prior to deployment, assess potential risks of algorithmic discrimination and implement a risk management policy … specifying principles and processes … reasonable in size, scope, and breadth, considering NIST’s AI Risk Management Framework, agency guidance, size and complexity of the developer or deployer….”
• Impact: Codifies ongoing governance frameworks at each operator, pushing companies to formalize AI policies that align with NIST, OSHA-style risk management, and sector regulators’ rules.
4. Digital service/social media platform duties (Sec. A551.010)
• Quote: “A digital service provider … or a social media platform … shall require advertisers … to agree to terms preventing the deployment of a high-risk AI system … that could expose users … to algorithmic discrimination or prohibited uses under Subchapter B.”
• Impact: Extends AI governance into the ad ecosystem, forcing platforms to police third-party AI-driven ads—potentially limiting targeted ad tech innovation.
Section D: Enforcement & Penalties
1. Attorney General enforcement (Sec. A551.102)
• Quote: “The attorney general has authority to enforce this chapter.”
• Impact: Centralizes AI enforcement authority in the AG, enabling civil investigative demands.
2. Notice and cure (Sec. A551.105)
• Quote: “Before bringing an action … the attorney general shall notify … not later than the 30th day before bringing the action … may not bring an action if … within the 30-day period, the developer or deployer cures the identified violation.”
• Impact: Offers a safe-harbor to come into compliance—valuable for startups and midsize businesses.
3. Civil penalties (Sec. A551.106)
• Quote:
– “$50,000–$100,000 per uncured violation” (non-prohibited uses)
– “$80,000–$200,000 per violation” (prohibited uses)
– “$2,000–$40,000 per day” for ongoing operation in violation
• Impact: High fines create a strong deterrent against noncompliance; their scale may strain small AI firms.
4. State agency sanctions (Sec. A551.107)
• Quote: “A state agency may sanction an individual licensed … for violations of Subchapter B … including suspension, probation, or revocation … and monetary penalties up to $100,000.”
• Impact: Regulatory boards (e.g., Medical Board, Banking Commission) can discipline licensees who violate AI prohibitions.
5. Consumer remedy (Sec. A551.108)
• Quote: “A consumer may appeal a consequential decision made by a high-risk AI system … and shall have the right to obtain … explanations of the role of the AI system … and the main elements of the decision taken.”
• Impact: Aligns with “right to explanation” doctrines, empowering individuals to challenge adverse AI decisions—potentially increasing litigation and operational transparency burdens.
Section E: Overall Implications
• Advance Responsible AI: By requiring NIST-aligned risk frameworks, impact assessments, and transparency, the Act sets a “trustworthy AI” baseline likely to improve public confidence.
• Increase Compliance Costs: Detailed pre-deployment reports, annual impact assessments, policy documentation, and mandatory disclosures will raise operational expenses—favoring larger or well-capitalized firms.
• Foster Innovation via Sandbox: The regulatory sandbox (Ch. 552) carves out a safe space for experimentation, enabling rapid prototyping without full compliance, provided safety and consumer-protection plans are in place.
• Preempt Local Laws: Sec. A551.152 preempts city or county AI rules, creating uniform statewide standards—simplifying compliance for companies operating across Texas.
• Centralize Oversight: The Artificial Intelligence Council (Ch. 553) and the Attorney General become focal points for guidance, rulemaking, and enforcement, which may accelerate policy iteration but also concentrate power.
• Encourage Ethical Research: By exempting open-source developers (Sec. A551.101(b)) who disclose weights and data, the Act protects academic and non-commercial research while still preventing misuse without modifications.
Ambiguities
• “Reasonable care” (throughout Subchapter A) is undefined—open to legal interpretation.
• The precise threshold for “substantial factor” in consequential decisions may require future rulemaking or case law to clarify.
• “Dark pattern” (Sec. A551.007(c)) is referenced by citation but not defined in text; conflicts with UX design standards may arise.
In sum, Texas’s proposed Act would be among the most comprehensive U.S. state laws to date, blending risk-based governance, consumer transparency rights, and sandbox innovation, at the price of significant new compliance requirements and penalties for AI developers, distributors, and deployers.
House - 2298 - Relating to a health care facility grant program supporting the use of artificial intelligence technology in scanning medical images for cancer detection.
Legislation ID: 20877
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. “Artificial intelligence technology”
– The bill repeatedly refers to “artificial intelligence technology” as the core object of the program. For example, Sec. A56.002(a) directs the Health and Human Services Commission (the commission) to “assist qualified applicants in using artificial intelligence technology to scan medical images for cancer detection.” (Bill, Sec. A56.002(a)).
– While the bill does not define “artificial intelligence technology” in technical terms, its repeated pairing with “scan medical images for cancer detection” implicitly scopes it to computer-vision or machine-learning systems that analyze radiology or pathology imagery.
2. “Qualified applicant”
– Defined at Sec. A56.001(2) as “a hospital or health care facility, including a federally qualified health center, located in this state that provides medical imaging services.” Thus, only clinical institutions that already perform imaging (e.g., X-ray, MRI, CT, mammography) are eligible to receive AI-focused grants.
3. Grant Program Scope
– Sec. A56.002 establishes “a grant program for artificial intelligence cancer detection” and authorizes annual awards (up to five per year) of up to $250,000 each (Sec. A56.004(b)(1)–(3)). The program runs through September 1, 2035 (Sec. A56.008).
Section B: Development & Research
1. Matching Funds Requirement
– Sec. A56.003(1) requires each applicant to “provide matching funds in an amount equal to at least 10 percent of the grant award amount.” This incentivizes applicants to invest their own capital, potentially promoting sustained R&D commitment to AI imaging.
2. Detailed Implementation Plan
– Sec. A56.003(2) obligates applicants to submit “a detailed plan for using the proposed artificial intelligence technology ... including the manner in which the grant money will be used and the total anticipated cost.” This may drive transparency around R&D budgets, system selection (in-house vs. vendor), and integration costs.
3. Physician Review Protocol
– Sec. A56.003(3) mandates “a plan for physician review of medical results identified through artificial intelligence to ensure accuracy and efficacy.” This emphasizes human-in-the-loop oversight, possibly shaping future research toward robust clinical validation and physician-AI interaction models.
4. Capacity & Throughput Metrics
– Sec. A56.003(4) requests “the number of patient records the proposed artificial intelligence technology is capable of scanning and the estimated time required for each scan.” By collecting throughput data, the commission can assess performance claims and drive comparative research on system scalability and speed.
Section C: Deployment & Compliance
1. Contractual Controls
– Under Sec. A56.004(a), each grant “must include conditions providing the commission with sufficient control to ensure the public purpose of improving public health is accomplished and the state receives a return benefit.” Though vague, this could translate into compliance checkpoints, performance milestones, or data-sharing obligations.
2. Annual Reporting Requirements
– Sec. A56.005 requires, within one year of award, a report detailing:
• Number of images scanned and cancer cases identified (Sec. A56.005(1));
• Changes in detection rates and time-to-diagnosis (Sec. A56.005(2));
• Comparative effectiveness versus traditional methods (Sec. A56.005(3));
• Recommendations for AI improvements (Sec. A56.005(4)).
– These reports will create a public record of real-world deployment outcomes, informing regulators, payers, and other health systems about the value proposition of AI imaging.
3. Funding & Donations
– Sec. A56.007 authorizes the commission to accept “gifts, grants, and donations from any source” and to use appropriated funds “to cover the costs of administering this chapter.” This flexibility could encourage private-sector partnerships or philanthropic support, potentially accelerating pilot deployments in underserved regions.
Section D: Enforcement & Penalties
– The bill contains no civil or criminal penalties for non-compliance beyond implied contractual remedies under the grant agreements (Sec. A56.004(a)). If a grantee fails to submit the required report or meet interim conditions, the commission presumably could claw back funds or withhold future awards, but those mechanisms are not explicitly detailed.
– No audit or inspection rights are spelled out, although the “sufficient control” language in Sec. A56.004(a) could be interpreted to grant the commission auditing authority.
Section E: Overall Implications
1. Advancement of AI in Medical Imaging
– By earmarking up to $1.25 million per year (5 grants × $250,000) for AI cancer-detection pilots, the state could spark innovation among hospitals and health centers that lack capital to trial advanced AI models.
2. Emphasis on Clinical Validation
– The mandated physician review plans and comparative‐effectiveness reporting may foster a culture of rigorous clinical trials, generating data on sensitivity, specificity, and workflow impact.
3. Public-Private Synergies
– The ability to accept external donations (Sec. A56.007) opens avenues for partnerships with AI vendors, academic labs, or philanthropic organizations, possibly accelerating technology transfer but also raising conflict-of-interest questions if not transparently managed.
4. Limited Regulatory Framework
– The bill focuses on incentives rather than direct regulation of AI systems. It does not address data privacy beyond existing health-care laws, nor does it impose certification standards for AI algorithms. This light-touch approach lowers barriers to entry but may leave gaps in patient-safety safeguards if future issues arise.
5. Sunset Clause
– The program expires in 2035 (Sec. A56.008). Depending on outcomes and data from the required reports, the legislature could renew, expand, or terminate the program, using empirical evidence generated under Sec. A56.005 to guide future AI policy.
Ambiguities & Possible Interpretations
– “Sufficient control” (Sec. A56.004(a)): Could be interpreted broadly (full data-sharing, algorithm access) or narrowly (financial audits only). Without rulemaking details, grantees may face uncertainty about compliance obligations.
– Definition of “artificial intelligence technology”: Absence of a technical definition leaves open whether only machine-learning/deep-learning systems qualify, or if traditional image-processing software could be funded. The executive commissioner’s rules (Sec. A56.006) will need to clarify eligible technologies.
House - 2400 - Relating to a prohibition on the use of artificial intelligence technology for classroom instruction.
Legislation ID: 20979
Bill URL: View Bill
Sponsors
House - 2491 - Relating to the use of certain algorithmic devices in the determination of residential rental prices.
Legislation ID: 21066
Bill URL: View Bill
Sponsors
House - 2818 - Relating to the artificial intelligence division within the Department of Information Resources.
Legislation ID: 204975
Bill URL: View Bill
Sponsors
House - 2874 - Relating to the inclusion of provenance data on content shared on social media platforms.
Legislation ID: 205029
Bill URL: View Bill
Sponsors
House - 2922 - Relating to use of artificial intelligence in utilization review conducted for health benefit plans.
Legislation ID: 205074
Bill URL: View Bill
Sponsors
House - 3512 - Relating to artificial intelligence training programs for certain employees and officials of state agencies and local governments.
Legislation ID: 131184
Bill URL: View Bill
Sponsors
House - 366 - Relating to required disclosures on certain political advertising that contains altered media; creating a criminal offense.
Legislation ID: 130203
Bill URL: View Bill
Sponsors
House - 3755 - Relating to biometric identifiers used in the performance of artificial intelligence.
Legislation ID: 131419
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused breakdown of H.B. ANo. A3755 (“the Bill”), organized into the sections you requested. Every claim is tied to a direct quotation from the text.
Section A: Definitions & Scope
1. “Artificial intelligence” (AI)
• Text: “(1) ‘Artificial intelligence’ means the use of machine learning and related technologies that use data to train statistical models for the purpose of enabling computer systems to perform tasks normally associated with human intelligence or perception, including computer vision, speech or natural language processing, and content generation.” (Section A1(a)(1), lines 9–14)
• Analysis: This definition explicitly targets systems that (a) “use data to train statistical models,” and (b) perform “tasks normally associated with human intelligence,” i.e. classic AI capabilities. It sets the scope of the Bill to any AI pipeline involving machine learning, computer vision, NLP, speech processing, or generative content.
2. “Biometric identifier”
• Text: “(2) ‘Biometric [ , “biometric ] identifier’ means a retina or iris scan, fingerprint, voiceprint, or record of hand or face geometry.” (Section A1(a)(2), lines 15–17)
• Analysis: By re-defining “biometric identifier,” the Bill clarifies which personal biological data fall under its rules when used in AI systems. This captures, for example, facial recognition or voice-based authentication.
3. Exemption scope for AI
• Text: “(f) This section does not apply to artificial intelligence or related training, processing, or storage, unless performed for the purpose of uniquely identifying a specific individual.” (Section A1(f), lines 18–21)
• Analysis: The Bill carves out a broad exemption for AI R&D and processing so long as it isn’t used to single out an individual. That means most AI research, model training, and bulk data processing are outside the statute—unless the goal is “uniquely identifying a specific individual.”
4. Triggering the Biometric Privacy provisions
• Text: “If a biometric identifier captured for the commercial purpose of artificial intelligence is used for another and separate commercial purpose, the person possessing the biometric identifier is subject to this section’s provisions for the possession and destruction of a biometric identifier and the associated penalties.” (Section A1(f), lines 21–24)
• Analysis: Even if a company captures biometrics under the AI exemption, any secondary commercial use immediately invokes the state’s biometric-privacy rules (which govern notice, consent, storage, destruction, and penalties).
Section B: Development & Research
There are no explicit R&D-funding mandates, reporting requirements, or data-sharing rules in this Bill beyond the exemption in Subsection (f). The key effect is to clarify that standard AI training and development do not require biometric-privacy compliance unless the system is used for individual identification.
Section C: Deployment & Compliance
1. Narrow Compliance Trigger
• Quotation: “unless performed for the purpose of uniquely identifying a specific individual.” (Section A1(f), lines 18–21)
• Impact: AI vendors building face-recognition or voice-recognition products that map a biometric sample to one person will have to comply with existing biometric-data rules. Companies building, say, generative-AI tools or general machine-vision solutions (without ID) are exempt.
2. Secondary-use Clause
• Quotation: “If a biometric identifier captured for the commercial purpose of artificial intelligence is used for another and separate commercial purpose … subject to this section’s provisions … and the associated penalties.” (Section A1(f), lines 21–24)
• Impact: Firms can’t sidestep notice/consent rules by saying “we only collected biometrics for AI training.” If they later reuse that data in an ID system or sell it, they face full biometric-privacy obligations. This discourages repurposing AI training datasets containing biometrics.
Section D: Enforcement & Penalties
The Bill refers generally to “this section’s provisions for … destruction of a biometric identifier and the associated penalties,” but does not amend or restate them. Those penalties reside elsewhere in Texas’s Business & Commerce Code, Chapter 503. The Bill:
• Reaffirms that violators who use biometric data to identify individuals (or repurpose AI-captured biometrics) remain subject to:
– Mandatory data-destruction timelines upon request or project end.
– Civil penalties for unauthorized collection, retention, or disclosure.
• Does not introduce new penalties or enforcement agencies specific to AI—rather, it relies on existing Chapter 503 enforcement mechanisms (private right of action, statutory damages, injunctive relief).
Section E: Overall Implications
1. Advances Clarity for AI R&D
By exempting non-identification AI activities from Chapter 503’s biometric-privacy rules, the Bill reduces regulatory uncertainty for researchers and startups working on general-purpose computer vision, speech, NLP, and generative AI. Most R&D—model training, benchmarking, inference—will not trigger notice/consent obligations.
2. Preserves Privacy Guardrails for Identification AI
Any system that seeks to map biometric data to an individual (for authentication, surveillance, ID verification) remains fully regulated. This dual-track approach balances innovation in “non-ID” AI with strong protections where the technology can impinge directly on personal privacy.
3. Prevents Data Repurposing
The secondary-use clause discourages “collect now, ask later” tactics. Enterprises must plan for final use cases of biometric data at collection time or face statutory penalties if they shift to identification applications.
4. Ambiguity & Edge Cases
– “Commercial purpose of artificial intelligence”: Does this phrase exclude non-commercial AI research conducted by universities or nonprofits? It arguably does, but a strict reading could unintentionally sweep in publicly funded projects.
– “Uniquely identifying a specific individual”: Could “identifying” be interpreted to include gender/age estimation or demographic clustering? If so, some inference tasks might fall under the rule even if they don’t single out a named person. Clarification may be needed.
In sum, H.B. ANo. A3755 primarily redefines the reach of Texas’s biometric-privacy statute in light of AI. It ensures that the collection and retention of biometric data for genuinely non-identification AI purposes are exempt, while maintaining robust safeguards for any system or use that ties biometrics to a unique human identity.
House - 3808 - Relating to the creation of the artificial intelligence advisory council and the establishment of the artificial intelligence learning laboratory.
Legislation ID: 131476
Bill URL: View Bill
Sponsors
House - 4018 - Relating to use of artificial intelligence in utilization review conducted for health benefit plans.
Legislation ID: 131685
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a section‐by‐section analysis of H.B. ANo. A4018, focusing exclusively on its AI‐related provisions, their scope, potential impact on stakeholders, and enforcement. All quotations are drawn directly from the bill text.
Section A: Definitions & Scope
1. “Artificial intelligence”
• Quotation (Sec. A4201.156(a), lines 8–12):
“’artificial intelligence’ means an engineered or
machine-based system that varies in autonomy and may, for explicit
or implicit objectives, infer from the input the system receives
how to generate outputs that can influence physical or virtual
environments.”
• Analysis: This is the only statutory definition of AI in the bill. It is broad (covers “engineered or machine-based systems” of any autonomy) and purpose-agnostic (explicit or implicit objectives). That breadth means both rule-based and modern machine-learning systems are covered.
2. Scope of Application
• Quotation (Sec. A4201.156(b), lines 13–15):
“A utilization review agent that uses an artificial intelligence-based
algorithm or other software tool for utilization review shall ensure…”
• Analysis: The bill applies specifically to “utilization review agents” (entities conducting prior authorizations, denial/approval decisions) when they employ “AI-based algorithm[s] or other software tool[s].” It does not regulate AI in other contexts (e.g., diagnosis support, billing).
Section B: Development & Research
(This bill contains no provisions that directly fund or mandate AI research, impose data-sharing requirements, or create reporting obligations for R&D. Its focus is purely on the operational use of AI in utilization review.)
Section C: Deployment & Compliance
Each deployment requirement below directly shapes how AI may be used by utilization review agents.
1. Input Data Requirements
• Quotation (Sec. A4201.156(b)(1), lines 16–23):
“the algorithm or tool bases its determination on the following information, as applicable:
(A) an enrollee’s medical or other clinical history;
(B) individual clinical circumstances as presented by the provider of record; and
(C) other relevant clinical information contained in the enrollee’s medical or other clinical record.”
• Impact: Prevents “black-box” AI that relies solely on population-level or non-specific data. Researchers and vendors must ensure systems ingest patient-specific clinical records.
2. Prohibition on Group-only Datasets
• Quotation (Sec. A4201.156(b)(2), lines 1–3):
“the algorithm or tool does not base its determination solely on a group dataset”
• Impact: Restricts purely statistical or demographic models that ignore individual profiles. This may limit use of large-scale pretrained models unless augmented with patient-specific data.
3. Compliance with Existing Law
• Quotation (Sec. A4201.156(b)(3), lines 4–6):
“the algorithm’s or tool’s criteria and guidelines comply with this chapter and applicable state and federal law”
• Impact: Embeds a compliance checkpoint. Vendors must map their AI’s decision logic to the Insurance Code and federal statutes (e.g., ACA nondiscrimination). Startups will need legal vetting; established vendors may adapt existing compliance frameworks.
4. No Overriding Provider Judgment
• Quotation (Sec. A4201.156(b)(4), lines 7–8):
“the algorithm or tool does not override the decision making of a physician or health care provider”
• Impact: Maintains clinician authority. AI becomes advisory, not determinative. Could require UI/UX changes so human reviewers must explicitly affirm or override AI suggestions.
5. Non-Discrimination Requirement
• Quotation (Sec. A4201.156(b)(5), lines 9–12):
“the use of the algorithm or tool does not discriminate, directly or indirectly, against enrollees in violation of state or federal law”
• Impact: Providers and insurers must conduct bias audits. Could spur routine fairness testing; liability risk if disparate impact is found.
6. Equitable Application & Rulemaking
• Quotation (Sec. A4201.156(b)(6), lines 13–16):
“the algorithm or tool is fairly and equitably applied, including in accordance with any applicable commissioner rules”
• Impact: Leaves room for the Texas Insurance Commissioner to issue implementing regulations—potentially tightening standards or specifying audit frequencies.
7. Transparency & Inspection
• Quotation (Sec. A4201.156(b)(7), lines 17–19):
“the algorithm or tool is available for review and inspection under Section 4201.154”
• Impact: Insurers must make AI tools accessible to regulators or third-party auditors. Could require submission of model documentation, source code, or performance logs.
8. Disclosure to Enrollees
• Quotation (Sec. A4201.156(b)(8), lines 20–25):
“the use and oversight procedures of the algorithm or tool are disclosed in writing to enrollees in the form and manner provided by commissioner rule”
• Impact: Creates a patient-rights dimension. Insurers must notify members that AI is used in their review process, potentially affecting member trust and consent.
9. Ongoing Monitoring & Revision
• Quotation (Sec. A4201.156(b)(9), lines 1–5 of page 2):
“the algorithm’s or tool’s performance, use, and outcomes are periodically reviewed and revised to maximize accuracy and reliability”
• Impact: Mandates a feedback-loop. Vendors must build monitoring dashboards; regulators may require periodic reports. Stiffer development lifecycle and validation protocols.
10. Purpose Limitation on Data Use
• Quotation (Sec. A4201.156(b)(10), lines 20–22):
“patient information is not used beyond its intended and stated purpose in accordance with state and federal law”
• Impact: Aligns with HIPAA and privacy laws. Research or commercialization outside utilization review would be prohibited unless separately authorized.
11. Safety & Harm Prevention
• Quotation (Sec. A4201.156(b)(11), lines 23–25):
“the algorithm or tool does not directly or indirectly cause harm to the enrollee other than assisting a utilization review agent in making an adverse determination.”
• Impact: Broadly wards against AI-induced unsafe decisions (e.g., recommending toxic therapies). Safety testing and hazard analyses become necessary.
12. Human-Centered Final Decision
• Quotation (Sec. A4201.156(c), lines 1–6 of page 2):
“A utilization review agent may not use an artificial intelligence-based algorithm… as the sole basis of a decision to wholly or partly deny, delay, or modify health care services… Only a physician or licensed health care provider… may determine medical necessity…”
• Impact: Reaffirms that AI advice cannot replace licensed professionals. Shields providers from delegating decisions to AI alone, preserving clinical responsibility.
Section D: Enforcement & Penalties
• The bill refers enforcement to existing utilization review enforcement under Sec. 4201.154 (not reproduced here). Non-compliance would subject agents to administrative penalties or ordering of corrective action under the Insurance Code.
• No new criminal or civil fines are specified; instead, failure to meet the listed conditions likely triggers the same remedies as any improper utilization review practice (license suspension, fines, mandated plan regimen).
Section E: Overall Implications
1. Advancement vs. Restriction
– The bill legitimizes AI in utilization review but under strict guardrails. It may encourage cautious adoption among established insurers but deter small startups lacking compliance budgets.
2. Impact on Stakeholders
– Researchers & Vendors: Must invest in explainability, bias testing, monitoring infrastructure, legal reviews.
– Insurers & Utilization Review Firms: Face higher operational costs for audits, disclosures, and human-in-the-loop processes.
– Providers & Physicians: Retain final authority; may need to engage more directly in reviews previously automated.
– Patients/Enrollees: Gain transparency rights and protections against AI-driven discrimination or harm.
– Regulators: Empowered to inspect AI tools, promulgate rules, and impose penalties for non-compliance.
3. Ambiguities & Rulemaking
– “Fairly and equitably applied” (Sec. (b)(6)) is open to interpretation and likely to be fleshed out by the Commissioner’s rules.
– The scope of “inspection” (Sec. (b)(7))—whether full source code, model weights, or only performance metrics—will require regulatory clarification.
Summary
H.B. ANo. A4018 neither bans nor unconditionally promotes AI; instead, it integrates AI into Texas’s existing utilization review regime under a comprehensive set of transparency, data-usage, nondiscrimination, and human-in-the-loop requirements. Its overall effect is to raise the compliance bar for AI deployment in health‐insurance decision-making, balancing innovation with patient safety and rights.
House - 421 - Relating to the creation of certain explicit deep fake material; providing a private cause of action.
Legislation ID: 19104
Bill URL: View Bill
Sponsors
House - 4390 - Relating to parental rights regarding the use of machine grading to score certain portions of assessment instruments administered to public school students in this state.
Legislation ID: 132050
Bill URL: View Bill
Sponsors
House - 4437 - Relating to a requirement that the Department of Information Resources implement and develop a system and database to authenticate and track certain digital content.
Legislation ID: 132088
Bill URL: View Bill
Sponsors
House - 4455 - Relating to the use of artificial intelligence by health care providers.
Legislation ID: 132106
Bill URL: View Bill
Sponsors
House - 4503 - Relating to electronic health record requirements; authorizing a civil penalty.
Legislation ID: 132151
Bill URL: View Bill
Sponsors
House - 4635 - Relating to disclosure of the use of artificial intelligence in the denial of insurance claims.
Legislation ID: 132279
Bill URL: View Bill
Sponsors
House - 5118 - Relating to a study on employer and state agency use of automated employment decision tools in assessing an applicants suitability for a position.
Legislation ID: 207247
Bill URL: View Bill
Sponsors
House - 5282 - Relating to the use of artificial intelligence to score certain portions of assessment instruments administered to public school students.
Legislation ID: 132881
Bill URL: View Bill
Sponsors
House - 5496 - Relating to the disclosure and use of artificial intelligence.
Legislation ID: 133086
Bill URL: View Bill
Sponsors
Senate - 1188 - Relating to electronic health record requirements.
Legislation ID: 23356
Bill URL: View Bill
Sponsors
Senate - 1411 - Relating to the use of artificial intelligence-based algorithms by health benefit plan issuers, utilization review agents, health care providers, and physicians.
Legislation ID: 210981
Bill URL: View Bill
Sponsors
Detailed Analysis
Here is an analysis of 89(R) SB 1411’s AI provisions, organized by your requested sections. All quotations are from the “Introduced version” you provided.
Section A: Definitions & Scope
1. “Artificial intelligence-based algorithm” (Insurance Code, Sec. 544.701(2))
• Text: “’Artificial intelligence-based algorithm’ means any artificial system that:
(A) performs tasks under varying and unpredictable circumstances without significant human oversight; or
(B) is able to learn from experience and improve performance when exposed to data sets.”
• Relevance: This is the bill’s core AI definition. It explicitly targets systems with autonomous task-execution (“varying and unpredictable circumstances without significant human oversight”) or machine-learning capabilities (“learn from experience and improve performance”).
2. Reuse in Occupations Code, Sec. 117.001(1)
• “’Artificial intelligence-based algorithm’ has the meaning assigned by Section 544.701, Insurance Code.”
• Relevance: Ensures the same AI definition governs both insurer and provider obligations.
3. “Utilization review agent” & “Health benefit plan issuer” (Sec. 544.701(5), (8))
• These definitions establish which entities must comply. They extend to insurers, HMOs, utilization-review vendors, etc., whenever they “use or may use” AI in utilization review (Sec. 544.703).
Section B: Development & Research
While the bill contains no explicit research-funding or open-data mandates, it does require submission of AI models and training data for state review.
1. Submission to TDI (Sec. 544.704(a)):
• Text: “A health benefit plan issuer shall submit an artificial intelligence-based algorithm and training data sets that are used or may be used in the issuer’s utilization review process to the department in the form and manner prescribed by the commissioner.”
• Impact on R&D: Issuers must package algorithms and underlying data sets for regulator review. This could impose costs on startups/vendors to prepare documentation and may discourage proprietary model use if data cannot be protected.
2. Certification process (Sec. 544.704(b)):
• Text: “The commissioner shall develop and implement a process for the department to certify that an artificial intelligence-based algorithm and related data sets … have minimized the risk of discrimination … and adhere to evidence-based clinical guidelines.”
• Impact: Creates a de facto regulatory approval pathway. Vendors may need to redesign or retrain models to satisfy anti-bias requirements and clinical-guideline alignment, potentially slowing innovation but raising safety standards.
Section C: Deployment & Compliance
1. Anti-discrimination rule for issuers (Sec. 544.702(a)):
• Text: “A health benefit plan issuer may not discriminate on the basis of race, color, national origin, gender, age, vaccination status, or disability through the use of clinical artificial intelligence-based algorithms in the issuer’s decision making.”
• Impact: Forces insurers to audit models for protected-class bias.
2. Disclosure requirement (Sec. 544.703):
• Text: “A health benefit plan issuer shall publish on a publicly-accessible part of the issuer’s Internet website and provide in writing to each enrollee, and any physician or health care provider … a disclosure regarding whether the issuer uses or may use artificial intelligence-based algorithms in the issuer’s utilization review process.”
• Impact: Increases transparency, enabling enrollees and providers to know when decisions are AI-aided. Could trigger consumer pushback or legal challenges if disclosures are vague.
3. Specialist review before adverse determination (Sec. 544.705):
• Text: “A utilization review agent that uses artificial intelligence-based algorithms to perform an initial review shall require that a specialist … open and document the utilization review of an individual’s clinical records … before making an adverse determination.”
• Impact: Imposes human-in-the-loop oversight. Slows down decision pipelines, adds labor costs, but may catch AI errors.
4. Provider obligations (Occupations Code, Sec. 117.002):
• Text: “A physician or health care provider may not discriminate on the basis of … through the use of clinical artificial intelligence-based algorithms when providing a medical or health care service.”
• Impact: Mirrors insurer anti-bias rules for clinicians. Providers using AI decision-support must similarly validate tools for fairness.
Section D: Enforcement & Penalties
1. Insurance side:
• Consumer Report Cards (Sec. 544.706): The Office of Public Insurance Counsel publishes “information identifying and comparing … the use of artificial intelligence-based algorithms by health benefit plan issuers and utilization review agents.” Public grading may create market pressure.
• No explicit monetary penalties in Subchapter O, but non-compliance with disclosure or submission requirements could trigger general insurance sanctions under Texas Insurance Code.
2. Provider side (Occupations Code, Chap. 117):
• Inspector General oversight (Sec. 117.003): May investigate “fraud and abuse related to use of artificial intelligence-based algorithms … and violations of this chapter.”
• Notice and hearing (Sec. 117.004): IG must notify within 15 days, and provider has 30 days to request a hearing.
• Sanctions (Sec. 117.005):
– “Suspension or revocation” of license;
– “Refusal … to issue a new license” (up to one year);
– Fine up to $5,000 per violation ($10,000 if intentional), not to exceed $50,000 per calendar year;
– Or any combination thereof.
• Impact: Clinicians face real professional and financial risk for AI‐driven discrimination. This creates strong disincentive to deploy unvetted AI tools.
Section E: Overall Implications
1. Drives Transparency & Oversight
– Mandatory AI disclosures (Sec. 544.703) and public report cards (Sec. 544.706) will put AI use under a public microscope.
2. Raises the Bar on Fairness
– Pre-deployment certification (Sec. 544.704) and explicit anti-bias provisions (Sec. 544.702, 117.002) will push vendors and providers to build or procure rigorously tested models.
3. Increases Costs & Slows Rollout
– Data-submission requirements, specialist sign-off (Sec. 544.705), and possible appeals/hearings (Sec. 117.004) add operational burden, especially for smaller startups or resource-strained clinics.
4. Limits Proprietary Secrecy
– Requiring training data disclosure may conflict with trade-secret protections, possibly chilling innovation or driving models offshore.
5. Creates a New Regulatory Regime
– The bill establishes a distinct AI review pathway in TDI and enforcement via the HHS Inspector General, signaling Texas’s interest in comprehensive AI governance in health care.
Ambiguities & Notes
– “Significant human oversight” (Sec. 544.701(2)(A)) is undefined. Is a single annual audit “significant”?
– “Adhere to evidence-based clinical guidelines” (Sec. 544.704(b)) leaves open which guidelines apply when there is no clear standard of care.
– The scope “may use” (Sec. 544.703, 544.704) is broad; issuers might need to certify every algorithm they could conceivably apply.
In sum, SB 1411 establishes one of the more detailed state-level AI frameworks in U.S. health care—focusing on fairness, transparency, and human oversight, but at the cost of added compliance complexity.
Senate - 1700 - Relating to the artificial intelligence division within the Department of Information Resources.
Legislation ID: 211265
Bill URL: View Bill
Sponsors
Senate - 1822 - Relating to the use of artificial intelligence-based algorithms in utilization review conducted for certain health benefit plans.
Legislation ID: 134269
Bill URL: View Bill
Sponsors
Senate - 1960 - Relating to digital replication rights in the voice and visual likeness of individuals; providing private causes of action; authorizing a fee.
Legislation ID: 211520
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a targeted analysis of 89(R) SB 1960 with respect to its provisions on AI-generated “digital replicas.” Every claim is tied to a direct quotation from the engrossed bill text.
Section A: Definitions & Scope
1. Definition of “Digital replica” (Sec. 651.001(1))
– “Digital replica” means “a newly created, computer-generated, highly realistic electronic representation that is readily identifiable as the voice or visual likeness of an individual…in which the individual did not actually perform or appear…or that is a version… materially altered.”
– Relevance to AI: By describing “computer-generated, highly realistic” likenesses not performed by the person, the bill is clearly targeting AI deepfakes and generative models that synthesize new images, audio, or video.
2. “Online service” and “online service provider” (Secs. 651.001(6)–(7))
– “any publicly accessible Internet website, online application…that predominantly provides a community forum for user-generated content” and “the owner of an online service.”
– Implicit AI focus: Many AI-powered platforms (e.g. social media sites with automated recommendation, hosting large AI-generated uploads) fall under these definitions and thereby gain new takedown duties.
3. Applicability (Sec. 651.002)
– “This chapter applies only to an individual who: (1) is a resident of this state; or (2) was a resident…on the date the individual died.”
– Scope: AI systems trained on non-Texan data or depicting non-Texans are outside this law’s subject; conversely, an AI system that uses a Texan’s likeness is covered.
Section B: Development & Research
– There are no funding, reporting, or data-sharing mandates aimed at AI R&D. The bill is purely rights- and liability-focused.
Section C: Deployment & Compliance
1. Exclusive right to produce or publish digital replicas (Sec. 651.053)
– “Except as provided by Section 651.054 … a person may not: (1) produce a digital replica without the written consent of the right holder; or (2) publish…or otherwise make available to the public a digital replica without the written consent of the right holder.”
– Impact: Any AI developer or end-user in Texas generating or distributing deepfakes of a protected individual must secure a license; otherwise they risk liability.
2. Permitted uses carve-outs (Sec. 651.054)
– “A person may use a digital replica without the right holder’s consent if … produced or used in a bona fide news… documentary… commentary, criticism, scholarship, satire, or parody; …fleeting or negligible manner; or in an advertisement…provided the digital replica is relevant to the subject.”
– Ambiguity: The term “materially relevant to the subject” could be interpreted narrowly (limiting incidental uses) or broadly (allowing more AI-driven marketing).
3. Online Service Provider duty to designate agent and remove content (Secs. 651.101–.102)
– “An online service provider shall designate an agent to receive notifications… and post… the name, address… of the designated agent.” (651.101(a))
– Upon notice, “remove or disable access to the material…as soon as technically and practically feasible.” (651.102(1))
– Impact: AI platforms, especially those hosting user-generated deepfakes, must adopt takedown procedures akin to the DMCA.
Section D: Enforcement & Penalties
1. Private causes of action (Secs. 651.202–.203)
– Injunctive relief: “An eligible plaintiff may…obtain … injunctive relief or other equitable relief.” (651.202)
– Statutory damages: “$5,000 per work…if the violator is an individual; $25,000 per work…if the violator is an entity other than an online service provider.” (651.203(f)(1))
– “Punitive damages” if violation is willful. (651.203(f)(2))
2. Safe-harbor for OSPs with reasonable belief (Sec. 651.203(g))
– “An online service provider that has an objectively reasonable belief that material…does not qualify as a digital replica may not be liable …exceeding $1 million.”
– Impact: Platforms get a cap on liability provided they act in good faith, which may ease compliance burden for startups.
3. False notice liability (Sec. 651.205)
– “A person who violates Section 651.103 [false takedown notice] is liable…for the greater of: (1) $5,000; or (2) actual damages and court costs and reasonable attorneys fees.”
– Impact: Discourages abuse of takedown notices by right holders or their agents.
Section E: Overall Implications
– Restrictive on Generative AI: By making “digital replica” creation and publication an exclusive right, the bill significantly restricts unlicensed AI usage of personal likenesses in images, audio, or video.
– Compliance Costs for Platforms: Online service providers must build notice-and-takedown systems, designate agents, and train staff—similar to DMCA compliance but now extended to AI-generated content.
– Effects on Startups vs. Established Vendors:
• Established platforms can absorb compliance costs and liability caps ($1 million) but may still face large volumes of takedown requests.
• Startups and new entrants may find the licensing requirement and risk of statutory damages prohibitive, potentially chilling innovation in AI deep-fake tools.
– Clarity vs. Ambiguity: While definitions are detailed, carve-outs (e.g. “materially relevant,” “fleeting or negligible manner”) leave room for legal interpretation, injecting uncertainty into what AI uses qualify for the exception.
– Interstate and International Reach: Although limited to individuals with Texas residence, AI models trained globally could inadvertently incorporate protected likenesses, creating conflicts with federal law and other state statutes.
In sum, 89(R) SB 1960 is a focused “deep-fake” law establishing property-style rights over AI-generated likenesses, imposing licensing and takedown requirements, and empowering private enforcement with substantial statutory damages. It reshapes the AI ecosystem by elevating individual control over AI-created content, while placing new operational and legal burdens on AI developers, platforms, and end users.
Senate - 1964 - Relating to the regulation and use of artificial intelligence systems and the management of data by governmental entities.
Legislation ID: 134399
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. “Artificial intelligence system” (Sec. 2054.003(1-a))
– Text: “machine-based system that … infers … a method to generate outputs … with varying levels of autonomy and adaptiveness after deployment.”
– Relevance: This catch-all definition brings virtually any ML model, expert system, generative AI, or decision-support tool under the bill. “Adaptiveness” explicitly includes systems that learn after deployment.
2. “Consequential decision” (Sec. 2054.003(2-c))
– Text: “a decision that has a material legal or similarly significant effect on the provision, denial, or conditions of a person’s access to a government service.”
– Relevance: Targets high-stakes automated decisions (e.g., welfare eligibility, parole risk scores).
3. “Heightened scrutiny artificial intelligence system” (Sec. 2054.003(6-a))
– Text: “an artificial intelligence system specifically intended to autonomously make, or be a controlling factor in making, a consequential decision.”
– Relevance: Triggers more rigorous rules for tools that drive high-impact outcomes.
4. “Principal basis” (Sec. 2054.003(11))
– Text: “use of an output … to make a decision without human review, oversight, involvement, or intervention; or meaningful consideration by a human.”
– Relevance: Defines fully automated vs. human-in-the-loop distinctions.
Section B: Development & Research
1. Inventory & Reporting (Sec. 2054.068(b)(2))
– Text: “inventory of … artificial intelligence systems, including heightened scrutiny artificial intelligence systems.”
– Impact: Forces agencies to catalog AI deployments; benefits transparency but creates reporting burden on small agencies.
2. R&D Sandbox Program (Sec. 2054.706)
– Text: “allow temporary testing … in a controlled, limited manner without requiring full compliance with otherwise applicable regulations.”
– Impact: Lowers barriers to pilot new AI capabilities in government, encouraging innovation. However, ambiguity in “controlled, limited manner” could slow vendor participation pending rule-making.
3. Public Sector AI Advisory Board (Sec. 2054.705)
– Tasks: “obtain and disseminate information on AI systems,” “facilitate shared resources,” “recommend elimination of rules that restrict innovation”
– Impact: Creates institutional forum to shape AI policy; likely to smooth procurement and sharing of best practices.
Section C: Deployment & Compliance
1. AI Code of Ethics (Sec. 2054.702)
– Text: “must include guidance … aligning with AI RMF 1.0 … addressing human oversight, fairness, accuracy, transparency, data privacy, security, redress, evaluations.”
– Impact: Imposes near-federal standards on all public AI use; helps standardize risk management but adds compliance costs.
2. Minimum Standards for Heightened-Scrutiny Systems (Sec. 2054.703)
– Text: “establish accountability measures … require assessment and documentation … before deploying the system; at the time any material change is made.”
– Impact: Ensures risk assessments, security reviews, and vendor training; may slow roll-out of critical systems but protect against unlawful harm.
3. Consumer Disclosure (Sec. 2054.707 & 2054.711)
– Text: “clear disclosure of interaction with [public-facing AI],” “standardized notice … include: general information about the system and data sources … measures taken to maintain compliance.”
– Impact: Transparency for end-users; potential UX friction. “Not required if a reasonable person would know” creates legal uncertainty.
4. Impact Assessments (Sec. 2054.708)
– Text: “system assessment that outlines: (1) risks of unlawful harm; (2) system limitations; (3) information governance practices.”
– Impact: Confidential pre-deployment reporting to the state; balances transparency with privacy but agencies/vendors must maintain internal expertise.
Section D: Enforcement & Penalties
1. Reporting Violations (Sec. 2054.709(a))
– Text: “agency or vendor … shall report the violation to the department … and the attorney general.”
2. Attorney General Powers (Sec. 2054.709(b))
– Text: “determine whether to bring an action to enjoin a violation.”
3. Contract Remedies (Sec. 2054.709(c–e))
– Process: Notice of violation → 31-day cure period → notice of intent to void → second 31-day cure → contract voidable.
– Impact: Strong commercial leverage to enforce compliance; vendors face de-barment (Sec. 2054.709(f)).
4. Public Complaint Portal (Sec. 2054.710)
– Text: “web page … to report … AI systems … unlawfully infringing … rights or financial livelihood.”
– Impact: Empowers citizens to trigger AG review; may lead to increased litigation or agency overhead.
Section E: Overall Implications
– This bill establishes a comprehensive governance regime for public-sector AI in Texas: from definitions through ethics, risk standards, sandbox testing, mandatory inventories, disclosures, and enforcement.
– For researchers and startups, the sandbox (2054.706) and advisory board (2054.705) offer opportunities to pilot technology and influence policy, but compliance costs for public procurements may favor established vendors with mature risk frameworks.
– For regulators, the new definitions (2054.003) and required reporting (2054.068, 2054.0965, 2054.708) create clearer jurisdiction but also a substantial administrative workload.
– End-users may benefit from greater transparency and recourse (2054.707, 2054.710) but face potential delays in service delivery as agencies implement human-in-the-loop controls and impact assessments.
– Ambiguities remain around “reasonable person” disclosure triggers (2054.707) and the precise scope of “controlled, limited” sandbox testing (2054.706(c)), which rule-making by the Department of Information Resources must clarify before September 1, 2025.
Senate - 228 - Relating to prohibiting the use of certain political advertising manipulated by generative artificial intelligence technology; creating a criminal offense.
Legislation ID: 209823
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a structured AI-focused analysis of 89(R) SB 228 (the “bill”), following your requested outline. All claims are anchored to the precise bill text.
Section A: Definitions & Scope
1. No standalone “Definitions” section is provided, but AI is implicitly defined via the phrase “generative artificial intelligence technology.”
• Citation: “Sec. 255.009(a)…intentionally manipulated using generative artificial intelligence technology…”
– This clause explicitly targets AI systems capable of producing or altering media (images, audio, video) in a realistic manner.
2. Scope of application is limited to “political advertising” that is “published, distributed, or broadcast”:
• Citation: “Sec. 255.009(a) A person…causes to be published, distributed, or broadcast political advertising…”
– The bill does not regulate AI per se but focuses on one use-case: AI-manipulated political ads.
3. Media covered: “image, audio recording, or video recording” of an officeholder or candidate’s “appearance, speech, or conduct.”
• Citation: “Sec. 255.009(a) …political advertising that includes an image, audio recording, or video recording of an officeholder’s or candidate’s appearance, speech, or conduct…”
– This covers still images, voice/audio, and video content, all common outputs of generative AI.
Section B: Development & Research
There are no provisions in this bill that address AI research, funding, data-sharing, or development processes. The bill’s sole focus is on the downstream use of AI in the political advertising context.
Section C: Deployment & Compliance
1. Prohibited conduct:
• Citation: “Sec. 255.009(a) A person commits an offense if the person causes to be published… political advertising that… has been intentionally manipulated using generative artificial intelligence technology in a manner that creates a realistic but false or inaccurate image, audio recording, or video recording…”
– Deployment of AI in political ads is not forbidden per se. Only those uses that produce “realistic but false or inaccurate” depictions are penalized.
2. Intent and effect requirements: two elements must be met to trigger the offense:
a. The depiction “to a reasonable individual… did not occur in reality.”
• Citation: “Sec. 255.009(a)(1) a depiction that, to a reasonable individual, is of the officeholder or candidate… but that did not occur in reality;”
b. The depiction creates “a fundamentally different understanding or impression” than the original.
• Citation: “Sec. 255.009(a)(2) a fundamentally different understanding or impression… than a reasonable individual would otherwise have obtained from the unaltered, original version…”
– Both the objective “reasonable individual” standard and the subjective requirement of “fundamentally different understanding” may pose compliance challenges:
• Ambiguity: What degree of alteration is “fundamental”?
• Enforcement complexity: Regulators must compare manipulated vs. original versions and assess public perception.
Section D: Enforcement & Penalties
1. Criminal penalty: Class B misdemeanor.
• Citation: “Sec. 255.009(b) An offense under this section is a Class B misdemeanor.”
– In Texas, a Class B misdemeanor carries up to 180 days in county jail and/or a fine up to $2,000.
2. Effective date and applicability: Applies only to advertising “published, distributed, or broadcast on or after the effective date” (September 1, 2025).
• Citation: “SECTION 2. The changes in law made by this Act apply only to political advertising that is published, distributed, or broadcast on or after the effective date… SECTION 3. This Act takes effect September 1, 2025.”
– No retroactive enforcement; ads before that date are governed by prior law.
Section E: Overall Implications for Texas’s AI Ecosystem
1. Restricts a high-visibility use-case (political advertising) rather than AI research or general commercial deployment.
– Startups and researchers working on generative AI models are unaffected unless their outputs are used in political ads that meet the prohibited criteria.
2. Chilling effect risk:
– Campaigns and vendors may over-filter or avoid AI-generated content altogether to reduce legal risk, potentially stifling legitimate uses (e.g., satire, commentary).
3. Enforcement burden on regulators and prosecutors:
– Determining what constitutes a “fundamentally different understanding” requires comparison against original media and likely expert testimony.
4. Alignment with broader “deepfake” concerns:
– This bill places Texas among states moving to criminalize deceptive AI-generated political content, signaling to vendors that transparency tools or watermarking may soon be necessary.
5. No carve-out for disclaimers or disclosures:
– Even if an ad clearly states “this is AI-generated,” it could still be penalized if it meets the two prongs—potentially chilling transparent, educational, or artistic uses.
In sum, SB 228 does not regulate AI development or deployment broadly but creates a narrowly tailored criminal prohibition on deceptive, AI-manipulated political advertising. Its real-world impact will hinge on how prosecutors and courts interpret the “reasonable individual” and “fundamental difference” standards when assessing AI-altered media.
Senate - 2373 - Relating to financial exploitation or financial abuse using artificially generated media or phishing communications; providing a civil penalty; creating a criminal offense.
Legislation ID: 211925
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of Texas S.B. 2373 (“Liability for Financial Exploitation”) organized into the requested sections. All points are tied to direct quotations from the enrolled bill text.
Section A: Definitions & Scope
1. “Artificial intelligence” (Sec. 100B.001(1))
– Text: “Artificial intelligence means a machine-based system that can, for a given set of explicit or implicit objectives, make predictions, recommendations, or decisions that influence real or virtual environments.”
– Analysis: This broad definition expressly captures any software or system that uses goal-driven algorithms. It targets AI systems generally rather than one narrow technique (e.g., neural networks), and thus sweeps in both established ML models and emerging AI approaches.
2. “Artificially generated media” (Sec. 100B.001(2))
– Text: “Artificially generated media means an image, an audio file, a video file, a radio broadcast, written text, or other media created or modified using artificial intelligence or other computer software with the intent to deceive.”
– Analysis: By including “other computer software,” the bill covers both AI-based deepfakes and non-AI editing tools when used to deceive. The requirement of “intent to deceive” creates a mental-state element.
3. “Phishing communication” (Sec. 100B.001(4))
– Text: “Phishing communication means an attempt to deceive or manipulate a person into providing personal, financial, or identifying information through e-mail, electronic communication, or other digital means.”
– Analysis: Although not AI-specific, this definition parallels AI-generated social engineering. The overlap may raise questions over when a phishing attack uses AI (e.g., personalized phishing via language models) versus traditional scripts.
Section B: Development & Research
– There are no provisions mandating state funding, reporting, or data-sharing for AI R&D. S.B. 2373 is purely liability-and-penalty focused; it does not directly regulate AI research or development workflows.
Section C: Deployment & Compliance
1. Civil cause of action for disseminators (Sec. 100B.002)
– Text: “A person is liable for damages resulting from a knowing or intentional dissemination of artificially generated media or a phishing communication for the purpose of financial exploitation.”
– Analysis: Any individual or entity that knowingly distributes deceptive AI media can be sued for damages (including “mental anguish and the defendant’s profits attributable,” Sec. 100B.002(b)(1)). Compliance for AI vendors may require content-monitoring systems or disclaimers preventing misuse.
2. Injunctive relief (Sec. 100B.002(c))
– Text: “A court…may issue a temporary restraining order or a temporary or permanent injunction to restrain and prevent the further dissemination of artificially generated media or a phishing communication to the claimant.”
– Analysis: Startups and platforms may need rapid-response takedown procedures to avoid court orders. The bill expressly preserves Section 230 immunity for platforms (Sec. 100B.002(d)).
3. Exempted intermediaries (Secs. 100B.002(d) and 100B.003(c))
– Text: “This section may not be construed to impose liability, for content provided by another person, on…(1) the provider of an interactive computer service…(2) a telecommunications service…(3) a radio or television station…”
– Analysis: Standard carve-out aligned with federal communications law. AI service providers hosting user-generated content remain protected from direct liability for third-party content.
Section D: Enforcement & Penalties
1. Civil penalties (Sec. 100B.003(a))
– Text: “A person who knowingly or intentionally disseminates artificially generated media or a phishing communication for purposes of financial exploitation is subject to a civil penalty not to exceed $1,000 per day the media or communication is disseminated.”
– Analysis: The Texas AG can seek up to $1,000/day. Entities deploying AI-generated messaging risk high cumulative fines if a campaign extends over days or weeks.
2. Criminal penalties (Penal Code Sec. 32.56)
– Text: “A person commits an offense if the person knowingly engages in financial abuse…through the use of artificially generated media…or by deceiving or manipulating another person into providing…information.”
– Grading (Sec. 32.56(c)):
• < $100 lost: Class B misdemeanor
• $100–< $750: Class A misdemeanor
• $750–< $2,500: State jail felony
• $2,500–< $30,000: Third-degree felony
• $30,000–< $150,000: Second-degree felony
• ≥ $150,000: First-degree felony
– Analysis: Integrates AI-driven deception into existing theft/abuse statute. Even a low-value deepfake scam can trigger a misdemeanor; higher-value fraud elevates to felony.
3. Confidential identity for victims (Sec. 100B.004)
– Text: “In an action…the court shall…allow a person who is the subject of the action to use a confidential identity…in all…documents…including any appellate proceedings…”
– Analysis: Lowers barriers for victims—particularly vulnerable populations (elderly, public figures)—to sue for AI-enabled scams without fear of exposure.
Section E: Overall Implications
1. Risk mitigation for AI vendors and platforms
– Although platforms remain immune for third-party content, any “knowing or intentional” participation in disseminating AI media for fraud is actionable. Vendors of generative-AI tools (e.g., text or image APIs) may need to implement misinformation detection, user verification, or usage-policy enforcement to demonstrate lack of “knowing” intent.
2. Chilling effect on benign AI uses?
– The “intent to deceive” requirement narrows liability to bad actors, but the broad sweep of “artificially generated media” could cause uncertainty for legitimate applications (educational content, satirical deepfakes). Ambiguity over what constitutes “intent” may deter innovators unless intake controls and disclaimers are robust.
3. Strong deterrent against AI-driven fraud
– Civil penalties ($1,000/day) and stiff criminal classifications for high-value schemes create meaningful financial and legal risks. This is likely to dissuade individual scammers and organized rings from using generative AI to impersonate or trick victims.
4. Gaps & ambiguities
– The link between “artificial intelligence” systems and “other computer software” in the “artificially generated media” definition could be interpreted so broadly that non-AI editing tools are swept in.
– No safe-harbor for researchers or developers who generate test deepfakes without “dissemination” or “intent to deceive.”
– The bill does not define “knowing” dissemination in the AI context—does a hosting provider that indexes a malicious video but lacks knowledge escape liability?
In sum, S.B. 2373 creates a strong liability framework targeting the misuse of AI-generated media for financial fraud, carving out safe harbors for intermediaries, establishing both civil and criminal penalties, and providing victim-friendly procedures. The law is likely to compel AI vendors, platforms, and integrators in Texas to invest in misuse-prevention controls (watermarks, user authentication, content filters) while providing a clear legal basis for victims to hold bad actors accountable.
Senate - 2373 - Relating to the financial exploitation or abuse of persons using artificially generated media or phishing communications; providing a civil penalty; creating a criminal offense.
Legislation ID: 134806
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of S.B. 2373 (89th R.S.), organized in the five requested sections. All quotations cite section numbers in the enrolled bill.
SECTION A: DEFINITIONS & SCOPE
1. “Artificial intelligence” (Sec. 100B.001(1))
• Text: “‘Artificial intelligence’ means a machine-based system that can, for a given set of explicit or implicit objectives, make predictions, recommendations, or decisions that influence real or virtual environments.”
• Relevance: This mirrors common technical definitions. By defining “AI” broadly, the bill ensures that any system capable of autonomous or semi-autonomous decision-making could fall under its rules.
2. “Artificially generated media” (Sec. 100B.001(2))
• Text: “‘Artificially generated media’ means an image, an audio file, a video file, a radio broadcast, written text, or other media created or modified using artificial intelligence or other computer software with the intent to deceive.”
• Relevance: Targets deepfakes and synthetic content used for scams. The “intent to deceive” qualifier is key—innocent uses of generative AI (e.g., data augmentation) are outside scope.
3. “Phishing communication” (Sec. 100B.001(4))
• Text: “‘Phishing communication’ means an attempt to deceive or manipulate a person into providing personal, financial, or identifying information through e-mail, electronic communication, or other digital means.”
• Relevance: Although not AI-specific, its inclusion alongside “artificially generated media” signals that automated or AI-enhanced phishing (e.g., spear-phishing via LLMs) is a target.
4. Financial exploitation / abuse definitions inherited from Finance Code § 281.001 and Penal Code § 32.55.
• These cross-references bring elder-fraud and vulnerable adult protections into the AI context.
SECTION B: DEVELOPMENT & RESEARCH
– There are no provisions in this bill that mandate AI R&D funding, reporting, or data-sharing. The bill is purely liability-oriented and does not regulate research processes, university labs, or public-sector AI development.
SECTION C: DEPLOYMENT & COMPLIANCE
1. Civil cause of action (Sec. 100B.002)
• Text, Sec 100B.002(a): “A person is liable for damages resulting from a knowing or intentional dissemination of artificially generated media or a phishing communication for the purpose of financial exploitation.”
• Implication: Any developer, distributor, or end-user who “knowingly” shares AI-generated scam content can be sued.
2. Injunctive relief (Sec 100B.002(c))
• Text: “A court … may issue a temporary restraining order or … injunction to restrain and prevent the further dissemination of artificially generated media or a phishing communication to the claimant.”
• Implication: Platforms or intermediaries could be compelled to remove or block content deemed fraudulent.
3. Immunity carve-outs (Secs 100B.002(d), 100B.003(c))
• Text: “This section may not be construed to impose liability, for content provided by another person, on: (1) the provider of an interactive computer service … (2) a telecommunications service … (3) a radio or television station licensed by the FCC.”
• Implication: Aligns with federal Section 230-type immunities. AI platform providers retain safe-harbor so long as they themselves are not the direct “knowing” disseminators.
SECTION D: ENFORCEMENT & PENALTIES
1. Private right of action (Sec. 100B.002)
• Remedies (Sec 100B.002(b)): “actual damages, including damages for mental anguish and the defendant’s profits attributable to the dissemination … and court costs and reasonable attorneys fees.”
• Effect on AI vendors: Facing treble-like exposure if a third party uses their tools to defraud. Could prompt stricter usage policies or integrated detection.
2. Civil penalties via Attorney General (Sec 100B.003(a))
• Text: “A person who knowingly or intentionally disseminates … is subject to a civil penalty not to exceed $1,000 per day the media or communication is disseminated. The attorney general may bring an action to collect the civil penalty.”
• Impact: Creates state enforcement in addition to private suits.
3. Criminal offense (Sec 32.56, Penal Code)
• Text, Sec 32.56(b): “A person commits an offense if the person knowingly engages in financial abuse: (1) through the use of artificially generated media …; or (2) by deceiving or manipulating … through e-mail, electronic communication, or other digital means.”
• Penalties (Sec 32.56(c)): Class B misdemeanor up to 1st-degree felony, depending on dollar amount.
• Implication: Developers whose tools are directly used in scheming may face criminal exposure if they “knowingly engage,” though “knowing” is a high mens rea.
SECTION E: OVERALL IMPLICATIONS
1. Emphasis on “knowing” intent reduces over-breadth but leaves ambiguity around AI vendor liability. Could a platform “know” that customers misuse generative-AI tools? That ambiguity may chill deployment of open models.
2. By preserving Section 230-style immunity, the bill focuses on individuals or businesses that disseminate scam media—not on intermediaries—thus allowing large cloud and social-media platforms to remain insulated.
3. End-users and victims gain a powerful new remedy: actual-damages plus profits and attorneys’ fees, on top of criminal charges. Startups offering generative-AI services will likely impose stricter validation and abuse-detection measures.
4. No requirements for watermarking, provenance, or pre-deployment audits. Enforcement remains after-the-fact, which may slow detection and remediation of AI-enabled fraud.
5. Overall, S.B. 2373 is a targeted anti-fraud measure, using AI-specific definitions but relying on traditional tort and criminal frameworks. It reshapes the liability landscape around generative AI and phishing without addressing broader R&D or governance issues.
Senate - 2473 - Relating to the use of an automated employment decision tool by a state agency to assess a job applicants fitness for a position.
Legislation ID: 160895
Bill URL: View Bill
Sponsors
Senate - 2490 - Relating to biometric identifiers used in the performance of artificial intelligence.
Legislation ID: 134903
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an analysis organized into the five requested sections. Every point is tied to the exact language of S.B. ANo. A2490 as introduced. Where the text is silent or ambiguous, I note that explicitly.
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Section A: Definitions & Scope
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1. “Artificial intelligence” definition
• Text (Sec. A1, amending § 503.001(a)(1)):
“"Artificial intelligence" means the use of machine learning and related technologies that use data to train statistical models for the purpose of enabling computer systems to perform tasks normally associated with human intelligence or perception, including computer vision, speech or natural language processing, and content generation.”
• Relevance: This is the sole statutory definition of AI. It explicitly calls out machine learning, statistical models, and core AI applications (vision, speech/NLP, content generation).
• Ambiguity: “Related technologies” and “tasks normally associated with human intelligence” are broad and could sweep in rule-based systems or classical algorithms if a court interprets them as “perception” tasks.
2. “Biometric identifier” definition
• Text (Sec. A1, amending § 503.001(a)(2)):
“"Biometric[, 'biometric] identifier' means a retina or iris scan, fingerprint, voiceprint, or record of hand or face geometry.”
• Relevance: Identifies the specific biometric data types that are regulated when used “in the performance of artificial intelligence.”
3. Scope carve-out for AI processing
• Text (Sec. A1, adding § 503.001(f)):
“This section does not apply to artificial intelligence or related training, processing, or storage, unless performed for the purpose of uniquely identifying a specific individual. If a biometric identifier captured for the commercial purpose of artificial intelligence is used for another and separate commercial purpose, the person possessing the biometric identifier is subject to this section’s provisions…”
• Relevance: Creates an explicit exemption for most AI uses of biometrics—so long as the AI system is not used to “uniquely identify” someone.
• Ambiguity:
– What constitutes “uniquely identifying”? Face recognition vs. broad demographic classification?
– “Another and separate commercial purpose” is vague: does fine-tuning a model on biometric data then delivering a non-ID service count?
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Section B: Development & Research
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The bill contains no direct funding mandates, reporting requirements, or data-sharing rules targeted at AI R&D. Its only impact on research is indirect, via the biometric-in-AI carve-out in § 503.001(f).
• Researchers processing biometric data for non-identification AI tasks (e.g., pose estimation, emotion recognition) would fall outside Chapter 503’s restrictions.
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Section C: Deployment & Compliance
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Again, there are no new certification, auditing, or liability rules expressly governing deployed AI systems. The only compliance change:
• If an AI vendor uses biometrics to identify an individual, then Chapter 503’s existing notice/disclosure consent and data-retention rules apply.
• Quotation: “This section does not apply … unless performed for the purpose of uniquely identifying a specific individual.” (§ 503.001(f))
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Section D: Enforcement & Penalties
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The bill does not create new enforcement agencies or penalty tiers. Instead, it leaves in place the existing enforcement and civil-penalty framework of Business & Commerce Code Chapter 503 for biometric identifiers used “to uniquely identify” someone.
• Penalty reminder (not repealed): Under § 503.005, a violation can lead to statutory damages of $25,000 per violation. The bill does not amend or repeal those monetary penalties.
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Section E: Overall Implications
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1. Advances:
• Clarifies that AI systems may train on and process biometric data so long as they do not perform identification, thereby reducing legal risk for a broad class of AI research and products (e.g., emotion analysis, demographic inference).
2. Restricts:
• Vendors wishing to deploy face-recognition, voice-ID, or fingerprint-ID services will still face the full Chapter 503 regime: notice, consent, secure storage, retention-limit, destruction, and potential damages.
3. Ambiguity & Compliance Risk:
• The term “uniquely identifying a specific individual” may require judicial or regulatory interpretation. Companies may over-comply (avoiding all biometrics) or under-comply (misclassifying identification uses) until guidance is issued.
4. Regulatory Efficiency:
• By carving out non-ID AI uses, the state reduces regulatory friction on AI startups and research labs that leverage biometric signals for lawful analytics, content generation, or user interfaces—intent on fostering innovation.
5. Unaddressed Gaps:
• No new data-governance standards for AI, no model-audit obligations, and no transparency requirements beyond consent for ID. The bill is narrowly scoped to biometric-in-AI and does not grapple with other AI risks (bias, robustness, explainability).
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In sum, S.B. A2490’s sole AI-related change is the narrow exemption in § 503.001(f). It confirms that general AI development and deployment using biometric inputs need not comply with Texas’s biometric-ID law unless the AI is used for personal identification. Beyond that, all existing Chapter 503 obligations remain in force for ID applications, and no new R&D or deployment rules are introduced.
Senate - 2567 - Relating to the deceptive trade practice of failure to disclose information regarding the use of artificial intelligence system or algorithmic pricing systems for setting of price.
Legislation ID: 134976
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an AI-focused analysis of S.B. 2567 (Texas DTPA amendment), organized in the structure you requested. All claims are tied to quoted bill text.
Section A: Definitions & Scope
1. “Artificial intelligence system”
– Text (lines 8–13): “(18) ‘Artificial intelligence system’ means the use of machine learning and related technologies … for the purpose of enabling computer systems to perform tasks normally associated with human intelligence or perception.”
– Relevance: This is a broad, technology-neutral definition that explicitly covers “machine learning,” “statistical models,” and tasks such as “computer vision, speech or natural language processing, and content generation.” It ensures any system using those techniques falls under the Act.
– Ambiguity: The phrase “and related technologies” could be read to include non-ML AI (e.g., rule-based systems), but it may also leave open exactly which “related” methods are covered.
2. “Algorithmic pricing systems”
– Text (lines 14–16): “(19) ‘Algorithmic pricing systems’ means any condition in which an artificial intelligence system when deployed generates recommendations on pricing.”
– Relevance: This carve-out makes clear that any AI used to suggest or set prices—dynamic pricing, surge pricing, personalized pricing—will trigger the new disclosure requirement.
3. New deceptive-practice provision
– Text (lines 17–19 of Section A2 and lines 18–19 of the bill): “(35) failure to disclose information regarding use of artificial intelligence system, or algorithmic pricing systems for setting of price.”
– Relevance: By adding this to the list of deceptive practices under the Texas Deceptive Trade Practices–Consumer Protection Act (DTPA), the bill obligates sellers to inform consumers when AI or algorithmic tools affect pricing.
Section B: Development & Research
– There are no provisions in S.B. 2567 addressing AI research funding, data-sharing mandates, or reporting requirements for R&D. The bill is strictly focused on consumer protection (disclosure) relating to pricing.
Section C: Deployment & Compliance
1. Disclosure requirement
– Text (Section 17.46(b)(35), lines 18–19 of Section A2): “failure to disclose information regarding use of artificial intelligence system, or algorithmic pricing systems for setting of price.”
– Impact on vendors: Any business using AI for pricing must update their terms of sale, websites, POS systems, or labeling to notify buyers that prices are influenced by AI. Failing to do so exposes them to DTPA claims.
– Impact on startups: Even small e-commerce platforms employing open-source ML for price optimization must create consumer-facing disclosures. This adds compliance overhead and may deter novel pricing experiments.
– Impact on end-users: Consumers gain transparency on when their price is set algorithmically, which could curb hidden “personalized” pricing but may also create confusion if not standardized (e.g., “We use an AI” vs. “Prices may vary by AI estimation”).
– Regulatory clarity: The requirement is broad—“any information” about use of AI must be disclosed. There is no specification of format, placement, or content level, leaving businesses uncertain how much detail suffices.
Section D: Enforcement & Penalties
1. DTPA enforcement mechanisms
– By folding the AI-disclosure rule into Section 17.46(b), violations become actionable under the existing Texas DTPA framework. That includes:
• Private causes of action by consumers (Section 17.50).
• Potential for treble damages if the violation is found to be “knowingly” made (Section 17.50(b)(1)).
• Recovery of attorney’s fees (Section 17.50(d)).
– Impact on businesses: Risk of class-action suits for failure to disclose AI pricing. Even inadvertent non-disclosure could trigger statutory penalties, unless the business can prove compliance.
2. Effective date and retroactivity
– Text (Section A3, lines 20–25): “The change in law made by this Act applies only to an act or practice that occurs on or after the effective date … An act or practice that occurs before the effective date … is governed by the law in effect on the date the act or practice occurred.”
– Impact: Companies have until September 1, 2025 (Section A4) to adjust practices. Conduct before that date is not subject to the new rule.
Section E: Overall Implications
1. Transparency vs. Innovation
– The bill pushes for consumer transparency in algorithmic pricing—a growing area of concern as dynamic and personalized pricing proliferate. But it does not define how disclosures should be made, creating uncertainty.
– Startups and small vendors may find the compliance burden disproportionate, potentially chilling innovative pricing strategies.
2. Narrow scope, broad effect
– Although limited to pricing, any use of AI in pricing triggers the rule. A company using ML to forecast demand and then adjust price automatically must disclose—even if the AI’s role is indirect.
– There is no carve-out for B2B transactions; “consumer” under DTPA can include small businesses, broadening the rule’s reach.
3. Enforcement through private litigation
– The bill relies entirely on private suits under the DTPA rather than administrative enforcement or technical standards. That may lead to patchwork litigation rather than consistent regulatory guidance.
4. Potential for future expansion
– By defining “Artificial intelligence system” so broadly, Texas sets up a vehicle for future DTPA provisions targeting other AI uses (e.g., content generation, decision-making), all under the same statutory headings.
In sum, S.B. 2567 amends the Texas DTPA to require disclosure whenever AI or “algorithmic pricing systems” influence price setting. It creates a civil-liability risk for non-disclosure, raises compliance questions around the form and substance of disclosures, and could chill certain AI-based pricing innovations pending further regulatory clarity.
Senate - 2966 - Relating to establishing a framework to govern the use of artificial intelligence systems in critical decision-making by private companies and ensure consumer protections; authorizing a civil penalty.
Legislation ID: 135333
Bill URL: View Bill
Sponsors
Senate - 2991 - Relating to the use of an automated employment decision tool by an employer to assess a job applicants fitness for a position; imposing an administrative penalty.
Legislation ID: 212526
Bill URL: View Bill
Sponsors
Senate - 382 - Relating to a prohibition on the use of artificial intelligence technology for classroom instruction.
Legislation ID: 209977
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is a targeted analysis of Texas SB 382 (89R, Introduced Version) organized per your requested structure. Every claim is anchored to the bill text. Where the bill is silent or ambiguous, I have noted that fact.
Section A: Definitions & Scope
1. No express definition of “artificial intelligence.”
– There is no “Definitions” section or terminology-definition clause. The bill simply uses the term “artificial intelligence technology” without defining it.
– Ambiguity: Without a statutory definition, it is unclear whether the term covers only machine-learning models or also rule-based systems, intelligent tutoring systems, generative AI, chatbots, etc.
2. Scope statement—educational institutions subject to the ban.
– “A school district or open-enrollment charter school may not use artificial intelligence technology…” (Sec. 28.0028)
– This clearly targets public K-12 institutions under Chapter 28 of the Education Code; private schools, higher education, and early childhood are outside its scope.
Section B: Development & Research
• No provisions for R&D funding, reporting, or data-sharing.
– The bill imposes an outright ban on instructional use but contains no mandate or encouragement for research.
– Because there are no clauses referencing “research,” “pilot programs,” “data,” “reports,” or “grants,” the bill neither advances nor curtails state-sponsored AI research directly. It merely restricts application in classrooms.
Section C: Deployment & Compliance
1. Prohibition on instructional deployment.
– Quotation: “A school district or open-enrollment charter school may not use artificial intelligence technology to: (1) provide instruction to students; or (2) replace or supplement a teacher’s role in providing instruction or interacting with students in a course of instruction.” (Sec. 28.0028(1)–(2))
– This applies to any deployment of AI tools as a teacher, tutor, grader, or conversational agent that interacts with students.
2. No compliance pathways or carve-outs.
– The text contains no exception for teacher-approved uses, adaptive learning platforms, or administrative tools.
– Ambiguity: Without carve-outs, it is unclear if using AI for lesson planning, grading homework, or administrative communication also falls under “replace or supplement a teacher’s role.”
Section D: Enforcement & Penalties
• No enforcement mechanism specified.
– The bill does not establish a penalty, fine, audit regime, or reporting requirement for violations.
– There is no reference to state agencies (e.g., Texas Education Agency) being empowered to enforce the ban.
• Effective date and retroactivity.
– “This Act applies beginning with the 2025-2026 school year.” (Sec. 2)
– “This Act takes effect immediately if it receives a vote of two-thirds… Otherwise… September 1, 2025.” (Sec. 3)
– These timing provisions govern when the ban becomes operative but do not establish enforcement procedures.
Section E: Overall Implications for the State’s AI Ecosystem
1. Restrictive stance on AI in K-12 education.
– By banning any use of AI for instruction or teacher augmentation, the state will effectively exclude AI-driven educational startups and research pilots from public schools.
– Established vendors of adaptive-learning platforms using AI will lose access to a major segment of the market.
2. Likely chilling effect on local innovation.
– School districts that might have experimented with AI-powered tutoring, automated grading, or intelligent chatbots will be barred from doing so in the public system.
– Researchers and startups lose a proving ground for in-school trials, potentially slowing state-level pedagogical AI innovation.
3. Ambiguities may yield uneven interpretations.
– Without a statutory definition of “artificial intelligence technology,” districts may struggle to determine what exactly is prohibited.
– The lack of enforcement detail could result in passive compliance rather than active oversight.
4. No direct impact on non-instructional AI.
– AI-driven administrative, facilities-management, or finance systems are likely unaffected, since the ban is limited to “provid[ing] instruction” or “replac[ing] or supplement[ing] a teacher’s role.”
In sum, SB 382 is a straightforward, prohibition-only measure targeting AI’s instructional deployments in public K-12 schools, with no accompanying research support, compliance framework, or enforcement mechanisms. Its greatest impact will be to foreclose school-district use of AI tutors, graders, or other classroom-facing tools as of the 2025-26 school year.
Senate - 668 - Relating to the disclosure of information with regard to artificial intelligence.
Legislation ID: 210249
Bill URL: View Bill
Sponsors
Detailed Analysis
Section A: Definitions & Scope
1. “Artificial intelligence” (Sec. 2003.001)
– Text: “’artificial intelligence’ means a machine-based system that: (1) when given a set of objectives by an individual, makes predictions, recommendations, or decisions that influence a real or virtual environment; and (2) uses inputs from an individual or machine to: (A) perceive or interpret a real or virtual environment; (B) use automation to analyze a perception or interpretation of a real or virtual environment; or (C) use models to infer or form opinions on information or a proposed action.”
– Analysis: This broad definition explicitly targets any system that ingests data, interprets environments, and outputs predictions or decisions—covering classical machine-learning models, deep-neural nets, computer vision systems, NLP models, and automated decision engines.
2. Applicability (Sec. 2003.002)
– Text “This chapter applies only to a person who: (1) uses artificial intelligence to provide services to an individual in this state, including: (A) answering questions; (B) gathering information; (C) summarizing information; (D) generating textual, audio, or visual material; or (E) providing information to be used in connection with a lending, underwriting, risk assessment, investing, or hiring decision; and (2) generated, or is more than 25 percent owned by a person who generated, at least $100 billion in total revenue…”
– Analysis: The bill narrowly scopes regulated entities to large, high-revenue AI providers, along with any smaller firm owned 25% by such giants. It covers consumer-facing Q&A/chatbots, data-gathering tools, summarization services, content generators, and AI in financial or HR decisions—explicitly capturing major public and private LLM deployers.
Section B: Development & Research
No provisions in this chapter impose direct R&D funding mandates or data-sharing obligations. The focus is on post-development disclosure and compliance. The bill does not address academic or pre-commercial lab research, thus leaving pure research activities largely unaffected.
Section C: Deployment & Compliance
1. Disclosure Requirements (Sec. 2003.003)
– Text: “A person regulated by this chapter shall disclose, on the person’s Internet website or in another location electronically accessible by an individual in this state: (1) the name of each artificial intelligence model used by the person; (2) a brief description of the functions and purposes of each model; (3) …each public or private third party that has provided input on an artificial intelligence model …; (4) a description of the specific input provided by each third party; and (5) any changes made … based on input provided by a third party.”
– Analysis: These disclosure obligations increase transparency for end-users, researchers, and regulators. Large AI vendors will need to track provenance of model training or tuning inputs and publicly publish model architectures or “function descriptions.” This could impede trade secrecy or intellectual-property claims, but enhance public auditability of bias or data-quality issues. Mandatory listing of third-party contributors may chill collaboration with non-commercial partners who wish to remain anonymous.
2. Exclusion for User Feedback (Sec. 2003.003(b))
– Text: “An individual who uses a service … and provides input on an artificial intelligence model is not considered a third party … if the individual’s input was provided: (1) in the individual’s personal capacity; and (2) based on the individual’s own experience as a user.”
– Analysis: This carve-out prevents day-to-day user feedback (e.g., correcting a chatbot answer) from triggering disclosure, focusing the rule on research collaborators, corporate tuners, and data-labelers.
Section D: Enforcement & Penalties
1. Prohibited Retaliation (Sec. 2003.004)
– Text: “A person may not discipline, retaliate against, or otherwise discriminate against an individual who in good faith reports a suspected violation …”
– Analysis: Offers whistleblower protections for compliance staff and collaborators. Encourages internal auditing and reporting without fear of reprisal.
2. Cooperation with Attorney General (Sec. 2003.005)
– Text: “A person shall allow the attorney general to access the records of the person to the extent necessary to ensure … substantial compliance.”
– Analysis: Grants broad investigatory powers; large AI firms will need to maintain detailed logs of model updates, input sources, and disclosure records.
3. Deceptive Trade Practice & Remedies (Sec. 2003.006)
– Text: “(a) A violation of this chapter is a deceptive trade practice under Subchapter E, Chapter 17 … actionable under that subchapter. … (d) The remedies under this section are cumulative of other remedies provided by law.”
– Analysis: Violations can trigger lawsuits, civil penalties, and AG enforcement under Texas’s Deceptive Trade Practices–Consumer Protection Act. The cumulative aspect raises risk of multi-front litigation.
Section E: Overall Implications
• Transparency vs. Secrecy: By mandating public disclosures of model names, purposes, third-party contributors, and model changes, the bill strongly favors openness. This benefits researchers, civil-society auditors, and end-users seeking accountability, but may deter AI vendors from revealing proprietary methods or partnerships.
• Barrier for Entry: Only very large or large-subsidiary-owned firms are covered, so startups under $100 billion revenue avoid these obligations—providing a competitive moat for smaller innovators. Conversely, smaller vendors acquiring funding or partial acquisition by a large corporation would immediately fall under the law.
• Enforcement Risk: Civil-practice liability and AG’s investigatory reach impose significant compliance costs on regulated entities. Dedicated legal and compliance teams will be required to catalog disclosures, manage AG requests, and defend against DTPA suits.
• Research Ecosystem: With no direct research funding or data-sharing mandates, academic and non-commercial research remains largely untouched. However, collaborations between universities and covered firms will need clear agreements on disclosure of joint model inputs or changes.
• End-User Impact: Consumers gain clarity on which AI systems they interact with, the model’s intended uses, and influence of third-party data. This may drive user trust but also raise expectations that could lead to new consumer-rights claims if disclosures are incomplete or misleading.
Senate - 747 - Relating to public school policies and programs regarding the production or distribution of certain intimate visual material by public school students.
Legislation ID: 210328
Bill URL: View Bill
Sponsors
Senate - 815 - Relating to the use of certain automated systems in, and certain adverse determinations made in connection with, the health benefit claims process.
Legislation ID: 210395
Bill URL: View Bill
Sponsors
Detailed Analysis
Below is an analysis of SB 815’s AI-related language, organized per your requested structure. All citations refer to the enrolled bill text as printed in your prompt.
Section A: Definitions & Scope
1. “Algorithm” (Sec. 4201.002(1-a))
– “Algorithm” is defined as “a computerized procedure consisting of a set of steps used to accomplish a determined task.”
– Relevance to AI: This broad definition can cover everything from simple rule-based code to complex machine-learning pipelines. By anchoring later restrictions to “algorithms,” the bill implicitly sweeps in both traditional software and AI/ML systems.
2. “Artificial intelligence system” (Sec. 4201.002(1-b))
– Defined as “any machine learning-based system that, for any explicit or implicit objective, infers from the inputs the system receives how to generate outputs, including content, decisions, predictions, and recommendations, that can influence physical or virtual environments.”
– This is the most direct AI definition in the bill. It expressly covers supervised and unsupervised learning models, decision-support neural networks, and similar systems that “infer” outputs from inputs.
3. “Automated decision system” (Sec. 4201.002(1-c))
– “An algorithm, including an algorithm incorporating an artificial intelligence system, that uses data-based analytics to make, suggest, or recommend certain determinations, decisions, judgments, or conclusions.”
– This catch-all term ensures that any algorithmic or AI-driven decision support used in utilization review qualifies as an “automated decision system.”
Together, these definitions establish that any rule-based algorithm or AI/ML model falls within the scope of the new restrictions on utilization review.
Section B: Development & Research
– SB 815 contains no provisions on AI research funding, data-sharing mandates, or reporting requirements for AI R&D. All AI-related text is focused on definitions and downstream deployment in insurance utilization review.
Section C: Deployment & Compliance
1. Prohibition on AI-driven adverse determinations (Sec. 4201.156(a))
– “A utilization review agent may not use an automated decision system to make, wholly or partly, an adverse determination.”
– Impact: Health-plan reviewers cannot rely on any algorithm or AI model—even to augment human decisions—when denying coverage as “not medically necessary.” Startups or vendors marketing AI tools for utilization review must redesign or rebrand their offerings for administrative support only.
2. Regulatory audit authority (Sec. 4201.156(b))
– “The commissioner may audit and inspect at any time a utilization review agent’s use of an automated decision system for utilization review.”
– Impact: Regulators gain broad, unannounced audit powers over any deployed AI or algorithm in the utilization-review workflow. Vendors will need thorough audit logs, documentation, and compliance workflows.
3. Carve-out for non-adjudicative AI uses (Sec. 4201.156(c))
– “This section does not prohibit the use of an algorithm, artificial intelligence system, or automated decision system for administrative support or fraud-detection functions.”
– Impact: AI tools may still be used for back-office tasks (e.g., auto-routing of claims, invoice processing) or to flag potentially fraudulent claims—but not for final “adverse determinations.” Vendors can pivot to these permitted use cases.
4. Notice requirements (Sec. 4201.303(a)(3))
– The “notice of an adverse determination must include … a description of and the source of the screening criteria and review procedures used as guidelines in making the adverse determination.”
– Although not amended to mention AI explicitly, this clause (as renumbered) means that any human reviewer relying on algorithmically generated guidelines must disclose both the guideline and its provenance. If an AI-derived scoring system influenced the decision, insurers likely must identify the model’s name/source.
Section D: Enforcement & Penalties
– The bill does not establish new civil penalties or criminal sanctions specifically tied to AI misuse. Instead enforcement is through the Texas Department of Insurance’s existing authority to audit/inspect utilization review agents (Sec. 4201.156(b)).
– If an agent is found using a prohibited “automated decision system,” they may be subject to enforcement actions under Chapter 4201’s existing “unfair claim settlement practices” and possible fines or license suspension.
Section E: Overall Implications
1. Restrictive carve-out for AI in claims denial
– By flatly banning “automated decision systems” from making or contributing to adverse determinations, Texas sharply curtails the use of AI/ML in utilization review—a major growth area for health-tech startups.
2. Compliance costs for vendors and payers
– All utilization review agents will need to inventory their algorithms and AI tools, segregate functions clearly into permitted vs. prohibited uses, and prepare for unannounced regulatory audits.
3. Innovation redirect
– AI vendors must pivot toward administrative support and fraud detection if they wish to enter the Texas health-plan market before January 1, 2026. Those developing AI for clinical decision support will face either human-in-the-loop controls or outright prohibition.
4. Patient transparency
– The enhanced notice requirements (Sec. 4201.303(a)(3)) mean patients will see what criteria—human or algorithmic—are used to deny coverage. This may drive further demand for explainable AI or clear human rationale in adverse-determination letters.
In sum, SB 815 uses broad definitions of “algorithm,” “AI system,” and “automated decision system” to prohibit practically any AI-driven tool from participating in the claims-denial process, while still permitting non-decision support uses. The primary effect is to slow or reshape deployment of AI in utilization review, drive significant compliance work for payers and vendors, and boost transparency around decision criteria.
Senate - 815 - Relating to use of artificial intelligence in utilization review conducted for health benefit plans.
Legislation ID: 22988
Bill URL: View Bill
Sponsors