California SB-1047: Safe and Secure Innovation for Frontier AI Models Act

Operative Mechanics: A Compute-and-Cost Threshold Never Brought Into Force

SB-1047 passed both chambers of the California legislature in late August 2024 (Assembly 28 August, Senate concurrence 29 August) but was vetoed by Governor Newsom on 29 September 2024, so its mechanics describe a regime that never took effect. The bill targeted a 'covered model' defined in § 22602 conjunctively — trained using more than 10^26 integer or floating-point operations AND costing more than $100M (or fine-tuned at a cost above $10M) — not a disjunctive compute-or-cost trigger. Its central obligation was not pre-deployment third-party testing: a developer had to adopt a written safety and security protocol before training and submit an annual SELF-certified statement of compliance, exercising 'reasonable care' to prevent 'critical harm'; independent third-party audits were to begin only on 1 January 2026 (§ 22603). 'Critical harm' (§ 22602(g)) was defined across distinct categories whose thresholds differ: creation or use of a chemical, biological, radiological or nuclear weapon causing mass casualties (no dollar floor), and cyberattacks on critical infrastructure or autonomous criminal acts causing either mass casualties OR at least $500M in damage — so the $500M figure attaches specifically to the cyber and autonomous-crime categories, not to the CBRN category. Enforcement ran through the Attorney General (§ 22606) plus whistleblower protections (§ 22607), with no private right of action. Compute as the regulatory hook reflects the argument that it is uniquely 'detectable, excludable, and quantifiable' ¹.

Cross-Jurisdiction Position: A US State Mirror of EU Systemic-Risk Logic

As the first US state-level frontier-model safety bill, SB-1047 paralleled the EU AI Act's general-purpose/systemic-risk tier, which itself sits atop unstable terminology — Fernández-Llorca et al. (2025) trace the drift across 'AI system, general purpose AI system, foundation model, and generative AI' ², and Hulok (2025) notes such models challenge 'authorship, accountability, and control' ³. Where Brussels relied on a fixed 10^25 FLOP presumption, California paired a 10^26 threshold (§ 22603) with a $100M cost limb, hedging against compute alone. Lacking the EU's GDPR substrate — relevant given foundation models that 'memorize and leak' training data ⁴ — the bill leaned wholly on catastrophic-harm framing rather than data-protection grounding.

Key Fault Lines: Threshold Evasion, Redress Gaps, and the Veto Rationale

Critiques clustered on three axes. First, compute thresholds are gameable: Pistillo and Villalobos (2025) document 'enhancement techniques... capable of decreasing training compute usage while preserving... model capabilities' ⁵, so a $100M/10^26 trigger (§ 22603) risks under-capturing capable models. Second, redress was thin — § 22607–22608 offered whistleblower and AG enforcement but no individual remedy, against literature finding decision subjects need genuinely 'meaningful' contestation ^6,7. Third, Newsom's veto argued the threshold was a poor proxy that could miss smaller-but-dangerous systems while burdening frontier labs — a critique sharpened by accounts of biosecurity dual-use risk ⁸ that compute gates address only obliquely. These fault lines played out as an unusually public coalition fight: prominent researchers backed the bill (Yoshua Bengio, Geoffrey Hinton, Stuart Russell, Dan Hendrycks) alongside Elon Musk, while a comparably prominent bloc opposed it — Andrew Ng, Fei-Fei Li and Yann LeCun on the technical side, members of California's congressional delegation (Pelosi, Lofgren, Khanna), and industry actors including Andreessen Horowitz, Y Combinator and OpenAI — a split that made the veto as much a political-economy outcome as a technical judgement.

Implementation Trajectory: From Veto to the Enforceable SB-53 Successor

Although vetoed, SB-1047 is cited in nearly every 2024–2025 AI governance review as the most consequential US state intervention, shaping the agenda even in defeat. Re-introduction did not revive the original; instead author Sen. Wiener's pared-back successor, SB-53 (Transparency in Frontier AI Act), was signed by Newsom on 29 September 2025 — the first enforceable US state frontier-AI safety law, most provisions effective 1 January 2026 (Office of Governor Newsom 2025). The trajectory tracks the catastrophic-risk scholarship that animated the bill: warnings that governance 'lacks the mechanisms and institutions to prevent misuse and recklessness' ⁹, the decisive-versus-accumulative risk split ¹⁰, and proposed compute-triggered audit treaties ¹¹.

Cross-jurisdiction comparison

How peer instruments treat the topics California SB-1047: Safe and Secure Innovation for Frontier AI Models Act governs.

°= industry self-imposed voluntary framework. Comparing a voluntary code's "governs" tint with a binding regulation's "governs" tint flattens the legal-force distinction; use the instrument-page banner for the operative status of each.

Does this instrument’s approach work? — the social-science evidence

Aggregated over the 6 topics this instrument governs: whether each harm is empirically real, and whether the peer-reviewed evidence shows governance reduces it. The badge is the epistemic status of the evidence— “thin”/“absent” efficacy evidence is itself a finding (the “second silence”). Each epistemic-status label is Policy Window's editorial assessment of the cited evidence base (a structured classification), not a verdict any single source issues.

Of the 6 governed topics with a social-science evidence review, evidence that governance reduces the harm is established for 0, contested for 0, thin for 0, and absent for 6 — for most, no replicated study yet shows this instrument's approach works (the "second silence").

• Catastrophic & Existential Risk

  • Is the harm real?evidence: contested

    The catastrophic-uplift premise is genuinely contested: the empirical uplift studies that exist find current frontier models add little. RAND's red-team study found no statistically significant difference in the viability of bioweapon-attack plans produced with vs. without LLMs (Mouton, Lucas & Guest 2024), and OpenAI's 100-participant trial found GPT-4 gave at most a mild, non-significant accuracy uplift (mean +0.88 out of 10 for PhD experts, +0.25 for students; Patwardhan et al. 2024). Honest caveat: the harm is forward-looking, not yet observed — expert opinion on the catastrophic tail is sharply split (median AI researcher puts ~5% on extremely-bad/extinction outcomes, mean ~9-16% across differently-framed questions, n=2,778; Grace et al. 2024), and forecasters underestimated how fast risk-relevant capabilities (e.g. virology troubleshooting) actually arrived (Forecasting Research Institute 2025), so the relevant capabilities are a moving target rather than a settled magnitude.

    Sources: Mouton, Lucas & Guest 2024 (RAND RR-A2977-2, Operational Risks of AI in Large-Scale Biological Attacks: Results of a Red-Team Study); Patwardhan et al. 2024 (OpenAI, Building an Early Warning System for LLM-aided Biological Threat Creation); Grace et al. 2024 (Thousands of AI Authors on the Future of AI, arXiv:2401.02843); Forecasting Research Institute 2025 (Forecasting LLM-enabled Biorisk and the Efficacy of Safeguards)

  • Does governance work?evidence: absent

    There is essentially no impact evidence that catastrophic-risk governance reduces catastrophic risk, and structurally there cannot yet be: the harm is a low-probability civilisational tail event, so no controlled trial or before/after evaluation of a realised catastrophe is possible. The dominant instruments are recent, voluntary developer frameworks (Anthropic's Responsible Scaling Policy 2023; OpenAI's Preparedness Framework 2023) built on if-then capability thresholds the developers themselves describe as speculative and qualitative rather than validated risk thresholds. The closest evidence is adjacent and indirect: trained-in deceptive behaviours can persist through standard safety training (Hubinger et al. 2024) — a demonstration that current mitigation may be insufficient, not that any governance regime works — and Anthropic's documented loosening of earlier commitments (RSP 2025 dropped the original pledge to define higher-tier ASL evaluations before developing the corresponding models) illustrates that even the strongest voluntary regimes lack external enforcement or measured efficacy.

    Sources: Anthropic 2023 (Responsible Scaling Policy); OpenAI 2023 (Preparedness Framework); Hubinger et al. 2024 (Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training, arXiv:2401.05566); Hendrycks, Mazeika & Woodside 2023 (An Overview of Catastrophic AI Risks, arXiv:2306.12001)

• Compute-Threshold Reporting

  • Is the harm real?evidence: contested

    Whether training-compute (FLOP) is a defensible proxy for governance-relevant capability is genuinely contested in the literature. The strongest empirical pressure against it is algorithmic efficiency: Ho, Besiroglu, Erdil et al. (2024) estimate the compute needed to reach a fixed language-model performance level has halved roughly every eight months (95% CI ~5-14 months, i.e. ~3x/year), so any static FLOP-to-capability mapping decays quickly; Hooker (2024) argues FLOP measures operations rather than end-performance, since techniques such as fine-tuning, retrieval, chain-of-thought and tool use can add large capability gains without proportional training compute, and Ord (2025) shows inference-time scaling further decouples deployed capability from training compute. Honest caveat: defenders (Heim & Koessler 2024; Pilz, Heim & Brown 2025) note compute remains the most quantifiable, externally verifiable, and ex-ante measurable correlate of frontier capability currently available, while themselves conceding it is an imperfect proxy that should not be used in isolation — the disagreement is about durability and precision, not whether any correlation exists.

    Sources: Ho, Besiroglu, Erdil, Owen, Rahman, Guo, Atkinson, Thompson & Sevilla 2024, Algorithmic progress in language models, NeurIPS 2024 (arXiv:2403.05812; Epoch AI); Hooker 2024, On the Limitations of Compute Thresholds as a Governance Strategy (arXiv:2407.05694); Ord 2025, Inference Scaling Reshapes AI Governance (arXiv:2503.05705); Heim & Koessler 2024, Training Compute Thresholds: Features and Functions in AI Regulation (arXiv:2405.10799); Pilz, Heim & Brown 2025, Increased Compute Efficiency and the Diffusion of AI Capabilities (AAAI 2025; arXiv:2311.15377)

  • Does governance work?evidence: absent

    There is no rigorous evidence that compute-threshold reporting reduces harm or achieves its stated aim, because the regimes have not produced an evaluable record. The US 10^26-FLOP reporting obligation (Executive Order 14110, invoking the Defense Production Act) was revoked on 20 January 2025 (by EO 14148) before its recurring binding reporting rule was finalized — the implementing BIS notice of proposed rulemaking (Sept 2024) never took effect, so no durable reporting record materialized; and the EU AI Act's 10^25-FLOP systemic-risk obligations for general-purpose models only became applicable on 2 August 2025 (with transitional periods into 2027), so no outcome evaluation yet exists. Moreover the 10^25 figure is a rebuttable presumption sitting alongside qualitative high-impact criteria (Art. 51(1)(a) and (2), rebuttable under Art. 52(2)), not a validated risk cutoff. The closest analogue is the broader regulatory-disclosure-mandate literature (Fung, Graham & Weil 2007), which documents that transparency policies' effects on outcomes are highly heterogeneous and frequently ineffective or counterproductive absent enforcement and downstream use — implying that the reporting trigger working as intended is an open empirical question, not a documented result.

    Sources: U.S. Executive Order 14110 (2023), Sec. 4.2 (10^26 FLOP, Defense Production Act); revoked by Executive Order 14148 (Jan 20, 2025); EU AI Act, Reg. (EU) 2024/1689, Art. 51 (10^25 FLOP systemic-risk rebuttable presumption; applicable Aug 2, 2025); Fung, Graham & Weil 2007, Full Disclosure: The Perils and Promise of Transparency (Cambridge University Press)

• Foundation Models / GPAI

  • Is the harm real?evidence: contested

    Whether the foundation-model category maps to a coherent capability/risk tier is genuinely contested. The original case rests on scale-driven 'emergent abilities' that appear unpredictably above a size threshold (Wei et al. 2022; Ganguli et al. 2022 documented capabilities that are smoothly predictable in aggregate loss yet locally surprising), but Schaeffer, Miranda & Koyejo (2023, a NeurIPS Outstanding Paper) showed many 'emergent' jumps are artefacts of discontinuous metrics and dissolve under linear/continuous scoring — implying capability scales more smoothly than a sharp tier would suggest. Honest caveat: this is a live empirical disagreement about measurement, not a settled finding either way, and compute (the regulatory proxy) is an imperfect stand-in for capability or risk regardless of which side is right.

    Sources: Wei et al. 2022 (Emergent Abilities of Large Language Models, TMLR; arXiv:2206.07682); Schaeffer, Miranda & Koyejo 2023 (Are Emergent Abilities of Large Language Models a Mirage?, NeurIPS 2023, Outstanding Paper; arXiv:2304.15004); Ganguli et al. 2022 (Predictability and Surprise in Large Generative Models, ACM FAccT; DOI 10.1145/3531146.3533229)

  • Does governance work?evidence: absent

    There is no impact evaluation showing that GPAI/foundation-model governance reduces harm — the rules are too new (EU AI Act GPAI obligations and the 10^25-FLOP systemic-risk presumption only began binding on 2 August 2025) and the central regulatory lever is itself contested: Hooker (2024) argues compute thresholds are a shortsighted proxy because compute does not reliably track capability or risk, and the thresholds already diverge across jurisdictions (EU 10^25 vs. the now-rescinded US EO 14110's 10^26 operations, rescinded 20 January 2025). The mandated mitigation methods also lack validated efficacy: model evaluation and red-teaming face well-documented coverage limits and an 'audit gap' in the survey/position literature (behavioural testing cannot establish the absence of untested failure modes), and adversarial red-teaming repeatedly defeats deployed safeguards — the UK AI Safety Institute reports finding universal jailbreaks for every frontier system it has tested, and a large public agent-injection competition elicited policy violations across all 22 frontier models tested from ~1.8M attacks (Zou et al. 2025). Even compliant evaluation therefore cannot yet certify the safety the rules demand. (Caveat: this is an absence-of-evidence claim — no efficacy study has been done — not evidence the rules are ineffective.)

    Sources: Hooker 2024 (On the Limitations of Compute Thresholds as a Governance Strategy, arXiv:2407.05694); EU AI Act Arts. 51 & 55 (GPAI systemic-risk presumption, 10^25 FLOP; binding 2 Aug 2025); US EO 14110 (10^26-operation reporting threshold, rescinded 20 Jan 2025 by EO 14148); Zou et al. 2025 (Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competition / Gray Swan Arena, arXiv:2507.20526 — 22 frontier agents, ~1.8M attacks); UK AI Safety/Security Institute, Frontier AI Trends Report (universal jailbreaks for every system tested); METR, Common Elements of Frontier AI Safety Policies (2024)

• Open-Weight Frontier Release

  • Is the harm real?evidence: contested

    The empirical picture splits into two well-separated questions. (1) The MECHANISM that distinguishes open-weight release — that safety guardrails can be cheaply and irreversibly stripped once weights are public — is established: Qi et al. (2024) removed GPT-3.5 Turbo safety alignment by fine-tuning on only ~10 adversarially designed examples for under $0.20 (and the attack generalizes to Llama-2), and even purpose-built tamper-resistant safeguards (Tamirisa et al. 2025, TAR) were subsequently shown to be defeatable by adaptive fine-tuning (Qi et al. 2024, durability critique). (2) Whether this mechanism produces real-world CATASTROPHIC uplift is genuinely contested and, for the headline biosecurity case, currently unsupported: RAND's red-team study found no statistically significant difference in the viability of bioweapon attack plans produced with versus without LLM assistance (Mouton, Lucas & Guest 2024), and OpenAI's 100-participant trial found at most mild uplift over an internet baseline (Patwardhan et al. 2024). Honest caveat: these null/mild results are time-stamped to 2023-2024 frontier capability and to biothreats specifically; the marginal-risk framework (Kapoor, Bommasani et al. 2024) concludes the evidence base is too thin to characterize marginal risk across most misuse vectors, so 'no measured harm yet' is not 'no harm.'

    Sources: Kapoor, Bommasani, Klyman, Longpre et al. 2024, 'Position: On the Societal Impact of Open Foundation Models', PMLR 235 / ICML 2024 (arXiv 2403.07918); Mouton, Lucas & Guest 2024, RAND RR-A2977-2, 'The Operational Risks of AI in Large-Scale Biological Attacks: Results of a Red-Team Study'; Qi, Zeng, Xie, Chen, Jia, Mittal & Henderson 2024, 'Fine-tuning Aligned Language Models Compromises Safety', ICLR 2024 (arXiv 2310.03693); Tamirisa et al. 2025, 'Tamper-Resistant Safeguards for Open-Weight LLMs', ICLR 2025 (arXiv 2408.00761); Qi, Wei, Carlini, Huang, Xie, He, Jagielski, Nasr, Mittal & Henderson 2024, 'On Evaluating the Durability of Safeguards for Open-Weight LLMs' (arXiv 2412.07097); Patwardhan et al. 2024, 'Building an early warning system for LLM-aided biological threat creation', OpenAI

  • Does governance work?evidence: absent

    There is no impact evaluation showing that any specific weight-release governance regime reduces downstream harm, because no binding regime has been implemented and measured: California SB-1047's release-conditioning framework was vetoed in September 2024, and the EU AI Act's open-source carve-outs (Recital 102, Art. 53(2)) exempt most open-weight models (those below the systemic-risk compute threshold) from the documentation obligations that would generate evaluable conduct. The structural obstacle is also documented: Kapoor, Bommasani et al. (2024) characterize open-weight release as effectively irreversible and poorly monitorable once weights are public, so post-release governance has little to act on. The closest analogue evidence — technology export controls — is mixed and points to circumvention: commentators argue blanket export controls on freely copyable open-source models cannot work (Just Security 2024), and independent analyses of the post-2022 semiconductor controls document displacement to less-regulated channels (smuggling, threshold-tuned chip variants, cloud access) rather than disappearance of activity (e.g., CSIS, FPRI 2024), suggesting recipient-restriction regimes face the same leakage problem for weights. (Caveat: this is analogical, not direct evidence about weight-release governance, which remains unmeasured.)

    Sources: Kapoor, Bommasani, Klyman, Longpre et al. 2024, 'Position: On the Societal Impact of Open Foundation Models', PMLR 235 (arXiv 2403.07918); California SB-1047 (2024, vetoed by Gov. Newsom 29 Sep 2024); EU AI Act Regulation (EU) 2024/1689, Recital 102 & Art. 53(2) open-source exemptions; Just Security 2024, 'Export Controls on Open-Source Models Will Not Win the AI Race'; CSIS, 'The Limits of Chip Export Controls in Meeting the China Challenge' and FPRI 2024, 'Breaking the Circuit: US-China Semiconductor Controls' (export-control circumvention analogue)

• Individual Redress

  • Is the harm real?evidence: established

    The premise behind redress — that affected people lack meaningful recourse against automated decisions — is real, but the flagship instrument is weaker than commonly assumed. Wachter, Mittelstadt & Floridi (2017) show GDPR creates only a limited 'right to be informed,' not a binding 'right to explanation' of specific decisions; and controlled work finds the explanations actually delivered do not measurably improve lay decision accuracy over showing the bare AI prediction (Alufaisan et al. 2021; and a 2022 meta-analysis by Schemmer et al. — screening 393 articles down to 9 in the final analysis — reports 'no effect of explanations on users' performance compared to sole AI predictions,' even though XAI overall had a positive effect). Honest caveat: the legitimacy/dignity value of being heard is empirically well established in the procedural-justice tradition even where outcome accuracy is unchanged, so 'redress fails' depends on which aim is measured.

    Sources: Wachter, Mittelstadt & Floridi 2017 (International Data Privacy Law 7(2):76); Alufaisan, Marusich, Bakdash, Zhou & Kantarcioglu 2021 (Proceedings of the AAAI Conference on AI 35(8):6618); Schemmer, Hemmer, Nitsche, Kühl & Vössing 2022 (AAAI/ACM AIES '22, meta-analysis)

  • Does governance work?evidence: absent

    There is no rigorous impact evaluation showing that mandated redress mechanisms (right-to-explanation, appeal, human-in-the-loop review) actually reduce erroneous or unfair automated decisions — the evidence that the rule works is itself missing. The closest experimental analogues are discouraging: explanations increase humans' acceptance of AI recommendations regardless of correctness (Bansal et al. 2021), and algorithm-in-the-loop oversight can introduce racial disparities and exhibit automation bias rather than reliably catching model errors (Green & Chen 2019). The procedural-justice literature (Tyler 1990; Lind & Tyler 1988) robustly supports a legitimacy and compliance benefit of fair process, but it measures perceived fairness, not reduction of the substantive decision harm redress is meant to cure.

    Sources: Bansal, Wu, Zhou, Fok, Nushi, Kamar, Ribeiro & Weld 2021 (CHI '21); Green & Chen 2019 (Disparate Interactions, ACM FAT* '19); Tyler 1990 (Why People Obey the Law, Yale Univ. Press); Lind & Tyler 1988 (The Social Psychology of Procedural Justice, Plenum Press)

• Transparency Obligations

  • Is the harm real?evidence: contested

    Documentation artifacts (model cards, datasheets) are well-specified as proposals and are genuinely adopted, but the empirical premise that mandated disclosure produces meaningful transparency is contested. Selbst & Barocas (2018) argue inscrutability and non-intuitiveness are distinct problems and that disclosing rules does not resolve the latter, and large-scale audits find documentation is sparsely and unevenly completed: a systematic analysis of 32,111 Hugging Face model cards (Liang et al. 2024) found environmental-impact, limitations and evaluation sections least often filled, and Bhat et al. (2023, 45 practitioners) found a substantial gap between the documentation proposal and actual practice. Honest caveat: the documentation frameworks themselves are real and adopted, so the dispute is about whether disclosure conveys decision-relevant information, not whether the artifacts exist.

    Sources: Selbst & Barocas 2018 (Fordham Law Review 87:1085-1139); Liang et al. 2024 (Nature Machine Intelligence, s42256-024-00857-z, 'Systematic analysis of 32,111 AI model cards'); Bhat et al. 2023 (CHI '23, 'Aspirations and Practice of ML Model Documentation', DOI 10.1145/3544548.3581518); Mitchell et al. 2019 (FAccT, Model Cards for Model Reporting); Gebru et al. 2021 (CACM 64(12):86-92, Datasheets for Datasets)

  • Does governance work?evidence: absent

    There is no rigorous impact evaluation showing that AI transparency mandates (model cards, training-data summaries) measurably reduce bias, misuse or accidents — the central regulatory assumption is empirically untested, partly because flagship mandates like EU AI Act Art. 53(1)(d) GPAI training-data summaries are only subject to AI Office enforcement/verification from 2 August 2026 (the obligation itself began 2 August 2025 for new models). The closest analogue, mandated consumer disclosure, shows small and context-dependent effects: Bollinger, Leslie & Sorensen (2011) found mandatory calorie posting cut average calories per transaction by about 6%, while Loewenstein, Sunstein & Golman (2014) review evidence that disclosure effects are frequently diminished or even reversed by limited attention and often change provider rather than recipient behavior. These are analogues, not AI studies; no study demonstrates that AI transparency disclosure achieves its stated downstream safety aims.

    Sources: Bollinger, Leslie & Sorensen 2011 (AEJ: Economic Policy 3(1):91-128); Loewenstein, Sunstein & Golman 2014 (Annual Review of Economics 6:391-419, 'Disclosure: Psychology Changes Everything'); EU AI Act Art. 53(1)(d) GPAI training-data summary (obligation from 2 Aug 2025; AI Office enforcement from 2 Aug 2026)

Related debates — rival interpretations & counterevidence

Structured controversies where this instrument's provisions are the locus of disagreement. Each debate page lays out the competing positions with primary-source citations.

• Open-Source vs Closed-Source Frontier Models — Should the most-capable AI models be released under permissive licenses (open weights), or only via API / structured-access agreements? The dispute is foundational to nearly every frontier-AI governance instrument.
• Pause AI vs Accelerate Capabilities — Should the global community impose temporary or capability-conditional pauses on frontier-AI development, or should development accelerate with safety work conducted in parallel?
• Compute vs Behavioural Capability Thresholds — Should the regulatory trigger for 'frontier' / 'foundation' / 'systemic-risk' status be training-compute thresholds (objective + ex-ante observable), or behavioural-capability evaluation (more semantically meaningful but operationally costly)?

Related instruments

• EU AI Act · EU
• Executive Order 14110 on Safe, Secure, Trustworthy AI · US
• G7 Hiroshima AI Process Code of Conduct · G7
• Bletchley Declaration on AI Safety · global
• Seoul Declaration on Safe, Innovative and Inclusive AI · global
• NIST AI RMF Generative AI Profile · US
• Brazil AI Bill (PL 2338/2023) · BR
• Anthropic Responsible Scaling Policy (RSP) v2 · US
• OpenAI Preparedness Framework · US
• Google DeepMind Frontier Safety Framework · US
• Meta Frontier AI Framework · US
• EU General-Purpose AI Code of Practice · EU
• GSA Generative AI and Specialized Computing Infrastructure Acquisition Resource Guide · US
• California SB-53: Transparency in Frontier Artificial Intelligence Act (TFAIA) · US
• New York RAISE Act: Responsible AI Safety and Education Act · US