Anthropic Responsible Scaling Policy (RSP) v2

What the Policy Commits To

The RSP v2 (Oct 2024) is a unilateral, self-binding corporate framework rather than a statute, structured around AI Safety Levels (ASL) that tie escalating safeguards to demonstrated model capabilities. Its operative core is a capability-triggered tripwire: pre-deployment evaluations targeting CBRN uplift and autonomous AI replication thresholds, with ASL-3/ASL-4 standards gating both deployment and weight security. The framework's Capability Thresholds govern frontier model releases and explicitly address catastrophic and agentic-replication risk, echoing the dual-use biosecurity concerns mapped by ¹ at ². Its transparency commitments promise public safety determinations, and a Frontier Red Team plus US/UK AISI coordination operationalise evaluation. Compute functions only as one signal, not the trigger.

Standing Relative to Binding Law

As a voluntary code, the RSP carries no force-of-law obligation: it is enforceable only by Anthropic's own governance and reputational stakes, not by a regulator. This distinguishes it sharply from the EU AI Act's binding general-purpose-model regime under Regulation (EU) 2024/1689, whose risk-based architecture ³ and contested foundation-model terminology ⁴ impose statutory duties the RSP merely shadows. Yet the RSP's influence is regulatory-adjacent: it is a Seoul Frontier AI Safety Commitments signatory and is cited by name in EU AI Office GPAI Code of Practice drafts, positioning it as soft-law scaffolding. Its capability-rather-than-compute trigger also sidesteps the loophole problems ⁵ that plague statutory compute thresholds.

Critiques, Gaps, and the Agentic Frontier

The central critique of any RSP-style framework is self-judgement: the same firm sets, measures against, and may waive its thresholds, with no external veto. The ASL focus on autonomous replication and agentic evaluation engages live scholarship on agent governance ⁶, agent infrastructure for attribution and remediation ⁷, and emergent multi-agent failure modes — miscoordination, conflict, collusion ⁸ — but the RSP's single-model evaluation lens captures none of these system-level dynamics. Coverage is also thin on synthetic-content provenance and elections, which sit in Anthropic's separate acceptable-use policies, not the RSP itself; this matters given documented watermarking shortfalls ⁹ and the accumulative-risk argument ¹⁰.

Adoption Trajectory and Superseded Status

The RSP's most durable contribution is vocabulary: the ASL tier model was subsequently adapted by OpenAI's Preparedness Framework and DeepMind's Frontier Safety Framework, making it a first-mover template for industry self-regulation. Critically, however, the v2 (Oct 2024) language this entry pins is now superseded. RSP v3.0 (24 Feb 2026) was a comprehensive rewrite that replaced the binding ASL hard-limit with a Frontier Safety Roadmap of publicly-declared targets plus Risk Reports and independent external review; the current reference is v3.3 (26 May 2026) (Anthropic 2026). Analysts must therefore treat v2's ASL-3/ASL-4 threshold text as historical, two major versions out of date. The pivot from hard tripwires toward declared roadmaps and external review partly answers the self-judgement critique, while the weight-access controls in higher tiers remain a private analog to export-style restriction debates ¹¹.

Cross-jurisdiction comparison

How peer instruments treat the topics Anthropic Responsible Scaling Policy (RSP) v2 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 9 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 9 governed topics with a social-science evidence review, evidence that governance reduces the harm is established for 0, contested for 0, thin for 1, and absent for 8 — for most, no replicated study yet shows this instrument's approach works (the "second silence").

• Agentic AI Governance

  • Is the harm real?evidence: thin

    The capability that agentic governance targets — autonomous multi-step action — is real and rapidly, measurably advancing: METR finds the task length AI agents complete at 50% reliability has doubled roughly every seven months for the past six years (about 50 minutes for frontier 2025 models), and the UK AI Security Institute's first Frontier AI Trends Report (Dec 2025, >30 systems) reports models now finish hour-long software tasks >40% of the time versus <5% in late 2023. The distinct realized HARM from agency (as opposed to the underlying model) is, however, thinly documented: on consequential real-world tasks agents still fail the majority — Gemini 2.5 Pro completed only 30.3% of TheAgentCompany's 175 professional tasks (OpenHands scaffold, project leaderboard) — so the agency-specific harm magnitude is early and context-dependent rather than established at scale.

    Sources: Kwa, West, Becker et al. 2025 (METR; arXiv:2503.14499, 'Measuring AI Ability to Complete Long Tasks'); UK AI Security Institute 2025 (Frontier AI Trends Report, Dec 2025); Xu, Song, Zhou et al. 2024 (TheAgentCompany, arXiv:2412.14161); 30.3% figure per TheAgentCompany leaderboard (OpenHands)

  • Does governance work?evidence: absent

    There is no impact-evaluation evidence that agent-specific governance reduces agentic harm: the operative regimes — the EU GPAI Code of Practice (published July 2025, voluntary/non-binding), the Seoul Frontier AI Safety Commitments (2024, voluntary), and AISI agent evaluations — are 2024-25 vintage and have never been measured against an outcome. The scholarship itself has not settled the contested unit of regulation: Kolt (2025) argues for governing the agentic relationship via principal-agent and agency-law tools, while Chan, Ezell, Kaufmann et al. (2024) propose agent-specific visibility mechanisms (identifiers, real-time monitoring, activity logging) that remain proposal-stage and unevaluated — meaning the field has design proposals but, as with most frontier-AI rules, the evidence that any of them works is absent rather than merely thin.

    Sources: Kolt 2025 ('Governing AI Agents', 101 Notre Dame L. Rev., forthcoming; arXiv:2501.07913); Chan, Ezell, Kaufmann et al. 2024 ('Visibility into AI Agents', ACM FAccT 2024, pp. 958-973; DOI 10.1145/3630106.3658948); EU AI Office 2025 (GPAI Code of Practice, July 2025); Seoul Frontier AI Safety Commitments 2024

• 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 + Model-Weight Export Controls

  • Is the harm real?evidence: contested

    That compute controls materially constrain China's frontier-AI hardware access is empirically real and measured: by mid-2025 the US hosted ~75% of catalogued AI-supercomputer performance versus China's ~15% (Pilz, Sanders, Rahman & Heim 2025), corroborated by the Federal Reserve's estimate of ~74% US vs ~14% China high-end AI compute share (Haag, FEDS Notes, Oct 2025), and US prosecutions document large diversion networks (e.g. the ~$160M Alan Hao Hsu / Hao Global H100/H200 case — the first 'AI diversion' conviction, guilty plea Oct 2025, SDTX). The honest caveat is that the magnitude of the binding constraint is genuinely contested: DeepSeek's V3/R1 reached near-frontier capability at an order of magnitude less reported compute via algorithmic efficiency, and analysts argue the controls have simultaneously accelerated Chinese state-backed indigenization, so whether the controls slow capability (the actual aim) versus merely shift its cost structure remains unsettled.

    Sources: Pilz, Sanders, Rahman & Heim 2025 (Trends in AI Supercomputers, arXiv:2504.16026 / Epoch AI) — VERIFIED, gives US ~75% / China ~15%; Haag 2025 (FEDS Notes, 'The State of AI Competition in Advanced Economies', Federal Reserve, 6 Oct 2025) — VERIFIED, gives US 74% / China 14% / EU 4.8% of high-end AI compute; US v. Alan Hao Hsu / Hao Global 2025 (DOJ, SDTX; ~$160M H100/H200 diversion, guilty plea Oct 2025, first AI-diversion conviction) — VERIFIED

  • Does governance work?evidence: absent

    There is no rigorous impact evaluation showing that compute or model-weight export controls achieve their stated strategic aim of durably slowing frontier-AI capability diffusion to China — the regime is too recent, the counterfactual is unidentified, and the most ambitious instrument (the Jan 2025 BIS 'Framework for AI Diffusion', ECCN 4E091 covering model weights of closed models trained on >10^26 operations) was rescinded on 12-13 May 2025 before it ever took effect (its enforcement date was 15 May 2025), so the evidence that the rule works is itself missing. The closest analogue evidence base, the economic-sanctions evaluation literature, is sobering: Hufbauer, Schott, Elliott & Oegg (2007) coded roughly a third of their historical cases as 'successful' (their database covers ~170-200 cases since WWI; the disputed coding was ~40 of 115, ~34%), but Pape's reanalysis (1997/1998) argued the genuinely sanctions-attributable success rate was far lower (he recoded it to ~5 of 115, under 5%), and the broader literature finds efficacy decays as targets adapt and substitute — the precise dynamic export-control critics attribute to Chinese indigenization and smuggling. This is an analogue, not direct evidence on export controls.

    Sources: Hufbauer, Schott, Elliott & Oegg 2007 (Economic Sanctions Reconsidered, 3rd ed., Peterson Institute for International Economics) — VERIFIED; Pape 1997/1998 (Why Economic Sanctions Do Not Work, International Security 22(2), 1997; Why Economic Sanctions Still Do Not Work, International Security 23(1), 1998) — VERIFIED; BIS 2025 (Framework for Artificial Intelligence Diffusion, Federal Register doc 2025-00636 / 90 FR, eff. 13 Jan 2025; ECCN 4E091 model-weight control; rescinded 12-13 May 2025 before its 15 May effective date) — VERIFIED

• 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)

• International Coordination

  • Is the harm real?evidence: contested

    The DESCRIPTIVE premise is well-established: IR scholarship now treats global AI governance as a fragmented 'regime complex' of partially overlapping G7/G20/OECD/GPAI/UN/standards-body arrangements with no central hierarchy (Tallberg et al. 2023 — verified verbatim: 'the emerging governance architecture for AI can be described as a regime complex'; Cihon, Maas & Kemp 2020). But the implied HARM — that forum-shopping and regulatory arbitrage cause a measurable race-to-the-bottom or relocate AI development to lax jurisdictions — is largely theorized/anticipated rather than empirically demonstrated for AI; Tallberg et al. explicitly flag forum-shopping as a dynamic whose presence in the AI regime complex is an open empirical question ('Establishing whether these patterns and dynamics are key features also of the AI regime complex stand out as important priorities in future research'). Honest caveat: the strongest empirical arbitrage evidence comes from analogue footloose digital markets (e.g., ICO reallocation after US securities enforcement) — itself a mixed/contested literature — not from AI firms, so the magnitude of coordination-failure harm in AI specifically remains contested and under-measured.

    Sources: Tallberg, Erman, Furendal, Geith, Klamberg & Lundgren 2023 (International Studies Review 25(3): viad040); Cihon, Maas & Kemp 2020 (Should AI Governance be Centralised?, AIES '20: 228-234); Lancieri, Edelson & Bechtold 2025 (AI Regulation: Competition, Arbitrage & Regulatory Capture, Theoretical Inquiries in Law 26(1): 239-262)

  • Does governance work?evidence: absent

    There are essentially no impact evaluations showing that the negotiated-coordination mode (AI Safety Institute network MoUs, forum-shifting, multilateral declarations) actually produces regulatory convergence or reduces arbitrage — the AISI Network began only as a statement of intent at the Seoul Summit (Seoul Statement of Intent, 21 May 2024) and held its first operational meeting in November 2024, with no defined metrics or outcome studies, so these soft-law instruments are too new to have measurable effects. The closest analogue evidence is mixed and works through DIFFERENT mechanisms than this topic describes: Bradford's Brussels Effect documents de-facto convergence driven by market access rather than negotiated coordination, and the FATF transgovernmental-network literature shows peer-review mutual evaluation can drive AML convergence — but neither evaluates voluntary AI MoU networks, and FATF's effects come with well-documented unintended consequences (de-risking, financial exclusion). The plain finding: the evidence that AI-governance coordination 'works' is itself missing.

    Sources: Bradford 2020 (The Brussels Effect: How the European Union Rules the World, Oxford University Press); Nance 2018 (The regime that FATF built: an introduction to the Financial Action Task Force, Crime, Law and Social Change 69(2): 109-129; cf. Slaughter 2004, A New World Order, Princeton University Press); International Network of AI Safety Institutes — Seoul Statement of Intent toward International Cooperation on AI Safety Science (21 May 2024; network's first meeting San Francisco, Nov 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)

• Synthetic Content Provenance

  • Is the harm real?evidence: contested

    The harm provenance targets is real but concentrated, and the technical premise that the mandated signal survives is itself empirically shaky. Synthetic-media harm is well documented in two domains: non-consensual intimate imagery (Ajder et al.'s 2019 Deeptrace audit found 96% of deepfake videos were pornographic and effectively 100% targeted women) and impersonation fraud (the Arup case, ~US$25.6M / HK$200M lost via a deepfake video call). The honest caveat is twofold: a feared broad political-misinformation harm is not yet demonstrated at scale, and CS work shows invisible watermarks are removable in practice (Jiang, Zhang & Gong 2023, WEvade, evade detection via adversarial perturbation; Zhao et al. 2024 prove pixel-level watermarks are provably removable via regeneration attacks), so the provenance signal a rule would mandate is itself contested.

    Sources: Ajder, Patrini, Cavalli & Cullen 2019 (Deeptrace, 'The State of Deepfakes: Landscape, Threats, and Impact'); Jiang, Zhang & Gong 2023 ('Evading Watermark based Detection of AI-Generated Content', ACM CCS 2023); Zhao et al. 2024 (NeurIPS, 'Invisible Image Watermarks Are Provably Removable Using Generative AI'); Arup deepfake fraud (CNN Business, 2024-05-16, US$25.6M)

  • Does governance work?evidence: thin

    There is no impact evaluation showing that mandated provenance/labeling reduces synthetic-media harm; the major mandates (China's GenAI labeling Measures, effective 2025-09-01; EU AIA Art. 50, machine-readable marking) are too new and unevaluated, and the delivery layer is leaky: the C2PA spec's own Security Considerations document the strip-and-repost threat, and platform audits report C2PA/Content-Credentials metadata is stripped by essentially all major social platforms on upload (consistent with Imatag's 2018 finding that ~80% of uploaded images lose metadata, only ~15% retaining it). The closest analogue evaluation literature — Pennycook, Bear, Collins & Rand (2020), the 'implied truth effect' — gives reason for caution rather than confidence: labeling only some content can make unlabeled false content seem more credible, so a partial-coverage provenance regime could backfire.

    Sources: Pennycook, Bear, Collins & Rand 2020 (Management Science 66(11):4944-4957, 'The Implied Truth Effect'); China Measures for Labeling AI-Generated Synthetic Content (eff. 2025-09-01); EU AI Act Art. 50; Imatag 2018 metadata-stripping study (~80%); C2PA Security Considerations (spec.c2pa.org) on manifest removal

• 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 instruments

• EU AI Act · EU
• Executive Order 14110 on Safe, Secure, Trustworthy AI · US
• G7 Hiroshima AI Process Code of Conduct · G7
• NIST AI Risk Management Framework · US
• Bletchley Declaration on AI Safety · global
• Seoul Declaration on Safe, Innovative and Inclusive AI · global
• NIST AI RMF Generative AI Profile · US
• California SB-1047: Safe and Secure Innovation for Frontier AI Models Act · US
• Brazil AI Bill (PL 2338/2023) · BR
• OpenAI Preparedness Framework · US
• Google DeepMind Frontier Safety Framework · US
• Meta Frontier AI Framework · US
• White House Voluntary AI Commitments · US
• Singapore Model AI Governance Framework for Generative AI · SG
• Japan METI AI Guidelines for Business · JP
• 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