GSA Generative AI and Specialized Computing Infrastructure Acquisition Resource Guide

What the Guide Directs Agencies to Do

Issued April 29, 2024 (GSA, Generative AI and Specialized Computing Infrastructure Acquisition Resource Guide), the Guide operates through procurement plumbing rather than command. It routes generative-AI and foundation-model buys as a discrete acquisition category, mapping them onto existing governmentwide vehicles — MAS IT and the Best-in-Class GWACs — so agencies can channel and surface AI spend through established channels, though the primary text itself enumerates no Special Item Numbers (see the primary-text section below). This category-by-compute logic tracks why governance increasingly leans on the compute lever, which is comparatively "detectable, excludable, and quantifiable" ¹ and currently "the most suitable metric to identify GPAI models" ². Its substantive lever is supplier due-diligence framing: it poses questions on training-data provenance, evaluation results, and model documentation for agencies to weigh — the binding vendor-disclosure requirements arrived separately in OMB M-24-18 (see the primary-text section below). By embedding responsible-AI considerations into requirements derivation rather than statute, it helps agencies translate OMB M-24-10's minimum-practice expectations into contractible obligations — though it leans on category terms like 'foundation model' that remain definitionally unstable even where they are legislated directly, as the EU AI Act's shifting text shows ³.

What the Primary Text Actually Contains

A primary-source read of the archived text (the live ITVMO page has since gone offline) reveals an earlier, humbler document, and relocates claims commonly attributed to it. GSA released the guide April 29, 2024, a 180-day EO 14110 Section 10.1(h) deliverable, not in September (GSA news release, Apr. 29, 2024). The introduction labels it "version 1.0", offers "prompts to consider and frame your thinking" rather than "directive recommendations", and states: "This content is non-binding" (GSA GenAI Guide v1.0 Introduction, Apr. 29, 2024). Section 4 concedes "There is currently not a governmentwide Generative AI-only acquisition vehicle", routing buyers to whole vehicles - MAS IT (formerly Schedule 70, 7.5 million-plus offerings) and Best-in-Class GWACs (8(a) STARS III, Alliant 2, CIO-SP3, EIS, NASA SEWP, VETS 2) - and enumerates no Special Item Numbers; 54151S appears nowhere. It also maps agency-specific and non-FAR paths: Army CHESS, DHS EAGLE Next Gen, phased "Pilot IRS", DoD Tradewinds, CRADAs, Economy Act agreements, OTAs, and Challenge.gov prizes (GSA Guide v1.0 Sec. 4, 2024). Nor does it ship sample clauses: Section 3.4 offers supplier due-diligence questions instead - data provenance and quality, whether agency inputs are stored or "used to train another AI model", who owns outputs and the model at contract end (GSA Guide v1.0 Sec. 3.4, 2024) - and Section 3.8 asks agencies only to "consider provisions" requiring risk-monitoring deliverables (GSA Guide v1.0 Sec. 3.8, 2024). Its hardest edges are supply-chain cross-references: Section 889 at FAR 4.2102(a)(2), FY23 NDAA Section 5949 semiconductor prohibitions effective December 2027, the TAA clause at FAR 52.225-5, and DFARS 239.7602-2 / HSAR 3052.204-72 data-location rules (GSA Guide v1.0 Sec. 5.4, 2024). The binding vendor-disclosure regime - agencies "must consider" requiring sub-group performance metrics and training-data "source, provenance, selection, quality" - arrived separately in OMB M-24-18, covering solicitations 180-plus days out and exempting National Security Systems (OMB M-24-18, Sept. 24, 2024). That layer proved as policy-contingent as feared: M-25-22 rescinded and replaced M-24-18 under EO 14179, pivoting to vendor lock-in protections and pre-award testing of "high-impact AI" from September 30, 2025 (OMB M-25-22, Apr. 3, 2025). The upshot: the guide was a market map and question bank; the contractible teeth lived one OMB layer up, and that layer turned over within seven months.

Standing Relative to Binding Law

The Guide is non-binding on its own; it carries no independent legal force and is best read as connective tissue in the EO 14110 / EO 14179 and OMB M-24-10 federal-AI stack. Its authority is derivative and conditional: M-24-10 Attachment 1 supplies the binding minimum practices for safety- and rights-impacting AI — including human consideration and remedy for rights-impacting determinations — while the Guide merely helps agencies translate those into solicitations. The salience of that translation is amplified by how broadly these systems reach: roughly 80% of the U.S. workforce "could have at least 10% of their work tasks affected" by LLMs, which display "traits of general-purpose technologies" ⁴. Supply-chain provisions lean on the existing FAR Part 4, Subpart 4.21 framework, which already carries national-security overlays. Force therefore attaches only once language is incorporated into a contract, where it binds the vendor as a term. This indirection mirrors a broader law-and-policy hybrid pattern in AI governance, where soft guidance scaffolds harder obligations ⁵.

Critiques and Structural Gaps

The Guide's reliance on vendor-disclosed training-data provenance inherits unresolved upstream problems. Disclosure obligations presume providers can characterize corpora, yet foundation models 'memorize and leak pieces of training data' and so resist treatment as anonymous ⁶, while opt-out and rights-clearance mechanisms remain practically brittle post-LAION ⁷ and contested across copyright regimes ⁸ — terrain on which the research-TDM route is even proposed as a conditional 'safe harbor' for openly released models ⁹, underscoring how unsettled provenance obligations remain. SIN-based routing also assumes legible capability tiers, but compute and capability proxies are gameable through efficiency techniques that preserve performance while lowering reported compute ¹⁰. Most consequentially, redress is only implicit — pushed onto M-24-10 — leaving acquisition language thin on what makes contestation meaningful for decision subjects ^11,12.

Adoption Trajectory and Outlook

Because the Guide is operational rather than legislative, uptake hinges on agencies actually importing its due-diligence questions and suggested provisions into solicitation packages — the notes indicate agencies typically do, though the primary text itself ships no sample clauses and enumerates no SINs (see the primary-text section above), so any standardization channel, even absent a mandate, runs through the existing vehicles it maps rather than a dedicated SIN structure. Its durability is policy-contingent: as a creature of the EO 14110 / EO 14179 and OMB M-24-10 layer, it can be revised or hollowed by executive turnover faster than statute. Comparative pressure may push it toward firmer disclosure: EU debates over general-purpose and foundation-model documentation, and security-exemption critiques warning that meaningful oversight of policing and security AI is becoming 'extremely difficult' ¹³, foreshadow where US procurement terms on provenance, evaluation, and national-security carveouts (FAR Subpart 4.21) will face tightening or contestation — pressure echoed in findings that defence and national-security exclusions leave biometric and surveillance uses under-regulated ¹⁴.

Cross-jurisdiction comparison

How peer instruments treat the topics GSA Generative AI and Specialized Computing Infrastructure Acquisition Resource Guide 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").

• 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)

• National Security Carveouts in AI Regulation

  • Is the harm real?evidence: thin

    That civilian AI-governance instruments carve out national-security uses is black-letter and undisputed (EU AIA Art. 2(3); CoE Framework Convention Art. 3(2) on national-security activities, distinct from Art. 3(4) on national defence; US NSM-25 (Oct. 2024) as the national-security-track instrument fulfilling §4.8 of EO 14110); civil-society legal analysis argues a blanket exclusion is harder to square with a necessity-and-proportionality approach than a qualified one (Korff/ECNL 2022; Vogiatzoglou 2024). But whether the carveout itself produces concrete unredressed harm is empirically under-observed almost by construction — the secrecy it confers suppresses the very evidence needed to measure it. The closest analogue, national-security deference in the courts, shows the mechanism is real (the FISC granted all but eleven of 33,900 applications 1979-2012, a 99.97% approval rate; Sinnar 2022 documents downstream harms to securitized communities), yet Clarke (2014) shows that lopsided ex parte approval rates alone do not prove rubber-stamping, because rational case selection and pre-vetting produce similar rates in ordinary Title III wiretaps (99.93%) and delayed-notice warrants (99.6-99.8%) — so the magnitude of harm attributable to the carveout, as opposed to the legitimate secrecy of the domain, remains genuinely contested.

    Sources: Korff 2022 (ECNL Opinion on the implications of the exclusion of national security from AI legislation, Oct. 2022); Sinnar 2022 (Harvard Law Review Forum 136:59, 'A Label Covering a "Multitude of Sins": The Harm of National Security Deference'); Clarke 2014 (Stanford Law Review Online 66:125, 'Is the Foreign Intelligence Surveillance Court Really a Rubber Stamp?'); EPIC FISC statistics 1979-2012

  • Does governance work?evidence: absent

    There is no impact evaluation showing that any specific design of the national-security carveout — categorical exclusion versus parallel governance track versus civilian-compliance-with-override — measurably improves oversight or reduces harm relative to the alternatives; the question is argued doctrinally (Vogiatzoglou 2024; Korff/ECNL 2022) but has never been tested empirically. The closest analogue evaluation literature is on the parallel-track model already in use for intelligence surveillance (the FISC / FISA oversight regime), and even there the evidence that the mechanism delivers effective scrutiny is itself contested rather than established (Clarke 2014; Sinnar 2022). No direct evaluation exists because the carveouts are recent (EU AIA 2024, CoE Framework Convention 2024, US NSM-25 2024), enforcement actions are by design non-public, and private parties typically lack standing to challenge a specific exempt deployment — the structural features that make the harm hard to observe also make the governance impossible to evaluate.

    Sources: Vogiatzoglou 2024 (Verfassungsblog, 'The AI Act National Security Exception: room for manoeuvres?', 9 Dec. 2024); Korff 2022 (ECNL Opinion, exclusion of national security from AI legislation); Clarke 2014 (Stanford Law Review Online 66:125); Sinnar 2022 (Harvard Law Review Forum 136:59)

• 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)

• Training-Data Rights

  • Is the harm real?evidence: contested

    That foundation models ingest copyrighted and personal works without consent is undisputed; whether that ingestion produces legally cognizable reproduction harm is genuinely contested. The CS evidence that models can memorize and emit verbatim training text is robust and replicated — Carlini et al. (2021) extracted hundreds of verbatim sequences (including PII) from GPT-2, and follow-up work (Carlini et al., Quantifying Memorization, ICLR 2023) showed extraction scales log-linearly with model size and with example duplication. Honest caveat: verbatim reproduction is the exception, not the norm — the UK High Court held that Stable Diffusion's model weights never stored copies of the training images (defeating the secondary-infringement theory), and Getty abandoned its primary training-infringement claim at trial for lack of evidence, so whether the empirical phenomenon amounts to actionable harm (rather than transient, non-expressive use) remains the open question driving NYT v. OpenAI and parallel regimes.

    Sources: Carlini, Tramèr, Wallace, Jagielski, Herbert-Voss, Lee, Roberts, Brown, Song, Erlingsson, Oprea & Raffel 2021 (Extracting Training Data from Large Language Models, 30th USENIX Security Symposium); Carlini, Ippolito, Jagielski, Lee, Tramèr & Zhang 2023 (Quantifying Memorization Across Neural Language Models, ICLR 2023; arXiv:2202.07646); Getty Images (US) Inc & ors v Stability AI Ltd [2025] EWHC 2863 (Ch) (UK High Court, 4 Nov 2025 — no secondary infringement; primary training claim abandoned at trial); The New York Times Co. v. Microsoft Corp. & OpenAI (S.D.N.Y., No. 1:23-cv-11195; consolidated In re OpenAI Copyright Infringement Litigation, Apr. 2025; ongoing 2025-2026)

  • Does governance work?evidence: absent

    There is no impact evaluation showing that the CDSM Directive Article 4 TDM exception plus its Article 4(3) opt-out reservation regime actually reduces unlicensed ingestion or channels compensation to rightsholders — the evidence that the rule works as designed is itself missing. The only available evidence is early case law and doctrinal scholarship, which document the mechanism's contested operation rather than its success: in Kneschke v. LAION the Hamburg Higher Regional Court (on appeal, 10 Dec 2025) held that a rights reservation in natural language did NOT satisfy Article 4(3)'s machine-readability requirement, invalidating the opt-out (note: the first-instance Regional Court had left the Article 4 question largely open and the case ultimately turned on the Article 3 scientific-research exception, so this machine-readability holding is appellate and not yet settled — a further appeal to the Federal Court of Justice was permitted). Legal scholars characterize the Article 4 opt-out as practically difficult and unharmonized, with no observed market in TDM licences or systematic enforcement to evaluate.

    Sources: Kneschke v. LAION (Hamburg Regional Court, 27 Sept 2024, 310 O 227/23; on appeal Hamburg Higher Regional Court, 10 Dec 2025, 5 U 104/24 — opt-out held not machine-readable; further appeal to BGH permitted); Margoni & Kretschmer 2022 (A Deeper Look into the EU Text and Data Mining Exceptions, GRUR International 71(8):685-701); Quintais 2025 (Generative AI, Copyright and the AI Act, Computer Law & Security Review 56:106107)

• 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
• 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
• Anthropic Responsible Scaling Policy (RSP) v2 · 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
• OMB Memorandum M-24-10 (Advancing Governance, Innovation, and Risk Management for Agency Use of AI) · US
• California SB-53: Transparency in Frontier Artificial Intelligence Act (TFAIA) · US
• New York RAISE Act: Responsible AI Safety and Education Act · US