UN GA Resolution on Safe, Secure, Trustworthy AI

Operative Mechanics: Hortatory Architecture and the Limits of Consensus

A/RES/78/265, adopted by consensus on 21 March 2024, is a non-binding General Assembly resolution: its operative verbs are "calls upon," "encourages," and "emphasizes," creating no legal duties. Its substantive core frames AI through a development-rights and digital-divide lens (the development_rights_framing provision is the lone "governs"-grade commitment), urging member states to craft national strategies and bridge inter-state capability gaps. On safety, transparency, and the "trustworthy AI" rubric it speaks only implicitly, with no operative definitions, thresholds, or reporting machinery — silent on the gradual, accumulative societal erosion that risk scholarship argues governance must address alongside abrupt scenarios ¹. The consensus that gave it US and PRC co-sponsorship was bought precisely by this thinness: language operative enough to bind would have fractured the coalition. The instrument's leverage is therefore agenda-setting and norm-seeding, not compliance — a baseline whose value rests on the capacity-building and inclusion conditions a framework for evaluating such initiatives deems necessary for viable governance ², and against which later, harder UN steps are measured.

Position Against Binding Regimes and Rival UN Tracks

Set beside operative law, the resolution is a floor, not a ceiling. The EU AI Act (Regulation (EU) 2024/1689) supplies the enforceable transparency duties the UN text only gestures at — Art. 50 mandates synthetic-media marking and deepfake disclosure — yet an empirical audit finds compliance already lagging, with only 38% of generators watermarking adequately ³. China's deep-synthesis and generative-AI rules go further still on mandatory provenance ⁴. Against these the resolution adds no operative content but performs distinct work: it is the universal-membership forum where Global-South development framings are codified. A framework for evaluating global AI-governance initiatives stresses capacity-building and inclusion as conditions for viable governance ² — legitimacy that plurilateral instruments are less placed to confer.

Key Fault Lines: Aspiration Without Apparatus

The central critique is the gap between the resolution's breadth and its silence on mechanism. Its "shared concerns" language touches catastrophic risk without operative text, even as scholarship maps acute AI-bio dual-use threats demanding concrete governance pathways ⁵. Its implicit deepfake and provenance gestures sit far below the granular state-level patchwork already documented across 319 US bills ⁶ and below proposals to assign liability to foundation-model providers ⁷. Its digital-divide framing also risks naivety about sovereignty: even resourced efforts like Gaia-X reabsorb dominant US cloud providers ⁸, and LLMs encode their creators' ideologies ⁹ — structural asymmetries no hortatory text can close. Its SDG-mediated nods to environmental cost and worker displacement likewise lack the disclosure levers analysts deem essential ¹⁰.

Implementation Trajectory: Superseded as the UN's Leading Edge

By 2026 the resolution has been overtaken as the UN's frontier instrument while remaining in force as foundational text. A/RES/79/325 (26 Aug 2025) established an Independent International Scientific Panel on AI and a Global Dialogue on AI Governance, and on 12 Feb 2026 the GA seated the Panel's 40 members (vote 117-2) for a 2026-2029 term — moving from exhortation toward standing institutions (UN General Assembly 2026). The development-rights and capacity-building emphasis of A/RES/78/265 is the through-line these bodies inherit; whether they translate it into effect turns on the institutional-competency and contextual-fit conditions that govern whether such initiatives prove viable ². The open question is whether the Panel narrows the aspiration-apparatus gap by reaching the empirically tractable harms — labour effects are real but uneven ^11,12 — that the 2024 resolution could only name.

Cross-jurisdiction comparison

How peer instruments treat the topics UN GA Resolution on Safe, Secure, Trustworthy AI 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 4, and absent for 5 — for most, no replicated study yet shows this instrument's approach works (the "second silence").

• AI-Driven Worker Displacement

  • Is the harm real?evidence: contested

    AI-driven labour displacement is demonstrably real but localized rather than economy-wide as of 2025-2026. Causal microdata find measurable harm in directly exposed segments: a difference-in-differences study of the Upwork freelance market found that after ChatGPT's release, freelancers in more AI-exposed occupations (e.g. writing) saw ~2% fewer contracts and ~5% lower monthly earnings, with larger losses among previously high-skilled workers (Hui, Reshef & Zhou 2024). Effects concentrate in entry-level and highly-automatable roles while aggregate US employment and wages show little disruption through 2024-2025 — so macro-level harm remains genuinely contested even as targeted-segment harm is established; much deployment to date augments rather than substitutes, raising novice productivity ~34% in call-center work (Brynjolfsson, Li & Raymond 2025).

    Sources: Hui, Reshef & Zhou 2024 ('The Short-Term Effects of Generative AI on Employment', Organization Science); Brynjolfsson, Li & Raymond 2025 ('Generative AI at Work', Quarterly Journal of Economics 140(2):889); Acemoglu 2024 ('The Simple Macroeconomics of AI', NBER WP 32487); Autor 2024 ('Applying AI to Rebuild Middle Class Jobs', NBER WP 32140)

  • Does governance work?evidence: thin

    There are essentially no impact evaluations of governance specifically targeting AI-driven displacement; current responses (OECD/GPAI guidance, reskilling initiatives, safety-net proposals) are at the recommendation stage, so 'does AI-displacement policy work' is answered only by extrapolation from the broader displaced-worker literature. That analogue base is robust but shows modest, mixed results: Card, Kluve & Weber's (2018) meta-analysis of 200+ active-labour-market evaluations finds training has small/insignificant short-run effects that improve only over the medium-to-long run, US Trade Adjustment Assistance evaluations find largely neutral-to-negative earnings effects (Schochet et al. 2012), and the JTPA randomized evaluation found weak earnings effects for the dislocated-worker stream. Recent syntheses note retraining yields smaller gains precisely when workers move into high-AI-exposure occupations — so the evidence that standard tools reduce AI-displacement harm is thin and early.

    Sources: Card, Kluve & Weber 2018 ('What Works? A Meta-Analysis of ... Active Labor Market Program Evaluations', JEEA 16(3):894); Schochet et al. 2012 (Trade Adjustment Assistance Program impacts, Mathematica/USDOL); Bloom et al. 1997 (National JTPA Study, Journal of Human Resources); Brookings 2025 ('AI Labor Displacement and the Limits of Worker Retraining'); OECD 2023-2025 Employment Outlook

• 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)

• Deepfakes / Synthetic Content

  • Is the harm real?evidence: established

    The flagship harm — non-consensual sexual deepfakes — is empirically real and sharply gendered: content audits find ~96-98% of deepfake videos online are non-consensual pornography overwhelmingly depicting women, and a pre-registered 10-country survey (>16,000 people) found 2.2% reporting victimization and 1.8% perpetration of synthetic intimate imagery, with documented mental-health, career, and participation harms. By contrast, the parallel claim that political/informational deepfakes UNIQUELY deceive is contested-to-refuted: experiments find deepfakes about as (not more) credible than equivalent text/audio fakes, and a 56-paper meta-analysis (k=137, N=86,155) puts unaided human detection near chance — implying a detection problem more than an exceptional-persuasion one.

    Sources: Umbach, Henry, Beard & Berryessa 2024 (CHI '24, 'Non-Consensual Synthetic Intimate Imagery ... in 10 Countries'); Diel et al. 2024 (Computers in Human Behavior Reports 16:100538, deepfake-detection meta-analysis of 56 papers); Barari, Lucas & Munger 2025 (Journal of Politics 87(2), 'Political Deepfakes Are as Credible as Other Fake Media'); Flynn et al. 2022 (British Journal of Criminology, multi-country image-based sexual abuse study)

  • Does governance work?evidence: thin

    Direct impact evidence that deepfake governance reduces the targeted harm is sparse and, where it exists, discouraging: the one quasi-experimental evaluation (Cuevas & Horta Ribeiro 2025, synthetic-control across three platforms) found the U.S. TAKE IT DOWN Act's passage plus the MrDeepfakes shutdown did NOT suppress synthetic non-consensual imagery — posting rose above counterfactual baselines and displaced elsewhere. Technical enforcement is likewise unreliable: detectors fail to generalize to unseen generators (notably diffusion models) and are vulnerable to adversarial evasion, with in-the-wild accuracy well below benchmark figures. No rigorous evaluation yet shows a deepfake-specific law, takedown mandate, or watermarking scheme producing a sustained reduction in prevalence or harm.

    Sources: Cuevas & Horta Ribeiro 2025 ('Deepfake Pornography is Resilient to Regulatory and Platform Shocks', arXiv:2602.02754); 'Adversarial Reality for Evading Deepfake Image Detectors' (ICCVW 2025); TAKE IT DOWN Act, S.146 / Pub. L. 119-12 (2025); CRS Legal Sidebar LSB11314

• Development-Rights Framings

  • Is the harm real?evidence: thin

    Development-rights framing is a normative/doctrinal frame, so its empirical status splits: the underlying North-South asymmetry it responds to is real and documented, but the claim that a development-rights diagnosis is the correct one is contested doctrine, not a settled finding. The strongest empirical anchor is the exploitative-data-labour evidence — Miceli & Posada's (2022) multi-method qualitative study of Latin American annotation work (Foucauldian dispositif analysis of 210 instruction documents, 55 interviews, plus participant observation) found workers paid cents-per-task with strict surveillance and whose worldviews are subordinated to requesters' — which substantiates the extraction the frame names, building on the data-colonialism thesis (Couldry & Mejias 2019), and extended by comparative political-economy work on AI annotation 'data empires' (Wu, Muldoon & Xia 2025). Honest caveat: whether 'digital self-determination' or 'Global-South sovereignty' is the right operational response (and whether it conflicts with the EU AIA's rights-based design) is a conceptual/legal question with essentially no empirical evidence base — the frame is established as a critique, thin as a tested governance prescription.

    Sources: Miceli & Posada 2022, 'The Data-Production Dispositif' (Proc. ACM Hum.-Comput. Interact. 6, CSCW2, Art. 460:1-37); Couldry & Mejias 2019, 'Data Colonialism' (Television & New Media 20(4):336-349); Wu, Muldoon & Xia 2025, 'Global data empires' (Big Data & Society 12(2))

  • Does governance work?evidence: absent

    There is no rigorous impact evaluation showing that development-rights / digital-self-determination / sovereignty governance achieves its stated developmental or self-determination aims — the evidence that the frame 'works' as policy is itself missing, largely because the frame is recent, heterogeneous, and rarely instantiated in a single measurable instrument. The closest empirical literature studies one common operational proxy (data localization) and measures economic cost rather than the frame's goals: Ferracane, Kren & van der Marel's (2020) firm/industry productivity analysis finds data-policy restrictiveness associated with lower TFP in data-intensive downstream sectors, Ferracane & van der Marel's (2021) gravity analysis finds data restrictions inhibit trade in digital services, and Bauer, Lee-Makiyama, van der Marel & Verschelde's (2014) GTAP general-equilibrium estimates project GDP losses from localization across seven jurisdictions including Brazil and India. None tests whether sovereignty framing reduces extractive asymmetry or advances local AI capability — so claims on both the benefit and cost sides rest on weak or indirect evidence.

    Sources: Ferracane, Kren & van der Marel 2020, 'Do data policy restrictions impact the productivity performance of firms and industries?' (Review of International Economics 28(3):676-722); Ferracane & van der Marel 2021, 'Do data policy restrictions inhibit trade in services?' (Review of World Economics 157(4):727-776); Bauer, Lee-Makiyama, van der Marel & Verschelde 2014, 'The Costs of Data Localisation: Friendly Fire on Economic Recovery' (ECIPE Occasional Paper 3/2014)

• AI in Education

  • Is the harm real?evidence: established

    The documented harms of educational AI are empirically real and, for proctoring, replicated: a controlled audit of a proctoring tool used by at least ~1,500 institutions found significantly higher facial-detection failure (the trigger for 'suspicious' flags) for darker-skinned and female test-takers (Yoder-Himes et al. 2022), and a technical audit of 164 government-endorsed pandemic learning products found 89% engaged in data practices that risk or infringe children's rights, with most monitoring happening without the child's knowledge or consent (Human Rights Watch 2022). Honest caveat: the benefit side is genuine but highly sensitive to how outcomes are measured rather than uniform — Kulik & Fletcher's meta-analysis of 50 intelligent-tutoring evaluations found an overall median effect of 0.66 SD, but the average effect was 0.73 SD on locally-developed tests versus only 0.13 SD on standardized tests, so much of AI education's apparent value depends on the outcome measure used.

    Sources: Yoder-Himes et al. 2022, 'Racial, skin tone, and sex disparities in automated proctoring software', Frontiers in Education 7:881449; Human Rights Watch 2022, 'How Dare They Peep into My Private Life?' (164 EdTech products endorsed by 49 governments; 89% risked/infringed children's rights); Kulik & Fletcher 2016, 'Effectiveness of Intelligent Tutoring Systems: A Meta-Analytic Review', Review of Educational Research 86(1):42-78

  • Does governance work?evidence: thin

    There are essentially no rigorous impact evaluations showing that purpose-built governance of educational AI reduces the documented harms. The student-specific regime — California's SOPIPA (SB 1177, 2014, a model that more than 20 states adopted and ~33 considered) and the FTC's May 2022 COPPA ed-tech policy statement (which the agency itself said did not change existing requirements) — has near-zero documented enforcement and no published before/after evaluation of whether it changed vendor data practices or bias outcomes. The only documented remedies came not from education-specific rules but from generic legal levers: a $6.25M biometric-privacy class settlement under Illinois BIPA (Veiga v. Respondus, 2023) and a constitutional ruling that proctoring room-scans are an unreasonable search (Ogletree v. Cleveland State University, N.D. Ohio 2022, Calabrese J.) — neither of which is a replicable evaluation, and both reach private/state actors rather than the underlying demographic-bias harm.

    Sources: California SOPIPA (SB 1177, 2014); FTC Policy Statement on Education Technology and COPPA (adopted May 19, 2022); Veiga v. Respondus, Inc. ($6.25M BIPA class settlement, 2023; covers Illinois Respondus Monitor users Nov. 2015–June 2023); Ogletree v. Cleveland State University (N.D. Ohio 2022, Calabrese J., room-scan Fourth Amendment ruling)

• Environmental Impact of AI Training

  • Is the harm real?evidence: established

    The resource demands of AI compute are empirically documented at the model level: Strubell et al. (2019) quantified large-NLP training energy/carbon, Luccioni et al. (2023) estimated BLOOM's training at ~24.7 tCO2eq (dynamic power) rising to ~50.5 tCO2eq with manufacturing and deployment, Li et al. (2023) estimated GPT-3-scale training in US datacenters can evaporate on the order of hundreds of thousands of litres of freshwater (their central figure ~700,000 L), and Luccioni, Jernite & Strubell (2024) showed generative inference is markedly more energy-intensive per query than task-specific models; at the macro scale the IEA (2024) and de Vries (2023) document rapidly rising datacenter electricity demand. Honest caveat: absolute estimates vary by up to orders of magnitude with grid carbon intensity, hardware, utilisation and accounting boundaries, and cleanly attributing the AI-specific increment (versus general datacenter and crypto growth) remains genuinely contested — the IEA itself bundles AI with datacenters and crypto — so the existence of the footprint is established while its magnitude and trajectory are not.

    Sources: Strubell, Ganesh & McCallum 2019 (ACL Anthology P19-1355; 'Energy and Policy Considerations for Deep Learning in NLP'); Luccioni, Viguier & Ligozat 2023 (JMLR 24; BLOOM 176B carbon footprint, 24.7/50.5 tCO2eq; arXiv:2211.02001); Li, Yang, Islam & Ren 2023 (arXiv:2304.03271, 'Making AI Less Thirsty', later Comm. ACM 2025); Luccioni, Jernite & Strubell 2024 (ACM FAccT '24, 'Power Hungry Processing', DOI 10.1145/3630106.3658542); de Vries 2023 (Joule 7(10):2191-2194, DOI 10.1016/j.joule.2023.09.004); IEA 2024 (Electricity 2024)

  • Does governance work?evidence: absent

    There is no impact evaluation showing that any AI-specific environmental-governance instrument reduces energy, water or carbon use, because every named instrument is voluntary or non-binding and very recent: EU AI Act Art. 95 codes of conduct are explicitly optional with no sanctions, and NIST AI 600-1 and the G7 Hiroshima Code are guidance, not enforceable caps. The closest analogue evaluation literature is divided in a way that disfavours the voluntary form chosen here: rigorous reviews find voluntary environmental programs generally fail to produce significant abatement beyond business-as-usual (Koehler 2007; Morgenstern & Pizer 2007), whereas the one form with credible positive evidence is mandatory disclosure (Downar et al. 2021 found a UK carbon-reporting mandate cut emissions ~8% versus a control group) which the AI instruments do not yet impose, leaving the proposition that AI environmental governance works essentially untested.

    Sources: EU AI Act Art. 95 / Recital 142 (Reg. (EU) 2024/1689); NIST AI 600-1 (2024, GenAI Profile); G7 Hiroshima Process International Code of Conduct (30 Oct 2023); Koehler 2007 (Policy Studies Journal 35(4):689-722); Morgenstern & Pizer (eds.) 2007 (Reality Check, RFF Press); Downar, Ernstberger, Reichelstein, Schwenen & Zaklan 2021 (Review of Accounting Studies 26(3):1137-1175)

• 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

• Technological Sovereignty

  • Is the harm real?evidence: contested

    The structural fact that compute capacity is geographically concentrated is well-measured: Lehdonvirta, Wú & Hawkins find only ~33 countries host facilities with AI-accelerator hardware and roughly 24 have the capacity to train full-scale foundation models, the Stanford AI Index 2026 reports low-income countries collectively hold ~0.1% of global data-centre compute (the US hosting >10x any other nation), and Cottier et al. document amortized frontier-training cost rising 2.4x/year (95% CI 2.0-3.1x) toward $1B+ models by 2027. But this is a political-economy FRAME, not a documented harm, and the core contested claim of the topic, that the cost curve locks mid-sized economies OUT of capability, is empirically cut both ways: a feasibility study of Brazil and Mexico (Malagon et al. 2025) estimates usable (non-frontier) 10-trillion-token sovereign models are fiscally viable at roughly $8-14M on H100 hardware, and DeepSeek-style efficiency gains (V3 trained for ~$5.5M, ~11x less compute than Llama 3 405B) show frontier-adjacent performance at a fraction of prior compute, so whether domestic frontier-tier capability is foreclosed for middle powers remains genuinely unsettled.

    Sources: Lehdonvirta, Wú & Hawkins 2024 (Compute North vs. Compute South, Proceedings of the 2024 AAAI/ACM Conference on AI, Ethics & Society 7:828-838); Cottier, Rahman, Fattorini, Maslej & Owen 2024 (The Rising Costs of Training Frontier AI Models, arXiv:2405.21015); Stanford AI Index 2026 (Maslej et al., Stanford HAI); Malagon, Ulloa Ruiz, Sandoval Plaza, Rosario Bolívar, García Mesa & Alvarado Morales 2025 (The Feasibility of Training Sovereign Language Models in the Global South: A Study of Brazil and Mexico, arXiv:2510.19801)

  • Does governance work?evidence: absent

    There is no rigorous impact evaluation showing that technological-sovereignty policies (on-shore compute mandates, national foundation-model champions, talent-retention schemes such as EuroHPC AI Factories or India's IndiaAI Mission) actually deliver sustained domestic capability or strategic autonomy; these programs are recent, utilization and cost-per-GPU-hour are largely unpublished, and no counterfactual study exists. The closest analogue evidence base, the industrial-policy literature synthesized by Juhász, Lane & Rodrik, finds that properly-identified studies are more favorable than older correlational work suggested but that outcomes depend heavily on instrument design and structural context, and the older national-champion record warns of subsidized 'zombie' firms and government capture, so the closest analogue is mixed and the direct evidence that the sovereignty rule works is simply missing.

    Sources: Juhász, Lane & Rodrik 2024 (The New Economics of Industrial Policy, Annual Review of Economics 16:213-242); Ahmed & Wahed 2020 (The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research, arXiv:2010.15581); IndiaAI Mission (Indian Cabinet, March 2024); EuroHPC Joint Undertaking AI Factories (2024 regulation amendment; no published impact evaluation)

• 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)