Research preview. Independent expert review is pending. The worked analyses are hypothetical.
Method and corrections Interactive field model
Inspect the system around the AI system. The model is organized by the clauses of the definition: societies, the four objects, the benefits and harmful patterns governance aims at, and the conditions it races against. Apply a cross-cutting test, narrow by lifecycle stage or level, and inspect how definitions, claims, relationships and sources travel together.
Apply a cross-cutting test Whole model Power Legitimacy Distribution Adaptability
Narrow by lifecycle stage or level Lifecycle stage Any stage Research Data collection Model development Evaluation Procurement Deployment Monitoring Incident response Retirement Level Any level Community and organisational use Corporate Sectoral National Regional International Loading interactive controls. Concept links remain available. Field model · whole model
Groups follow the clauses of the definition; they are not a sequence or causal chain. Actual relationships appear in the inspector.
Jump to selected entity Societies …how societies should shape…
actor AI developer or provider actor Deploying organization actor Affected people actor Civil society and independent researchers Incentives …the incentives…
incentive Deployment pressure incentive Competitive race dynamics incentive Liability exposure incentive Procurement leverage
Institutions …institutions…
institution Regulator or public authority institution Internal safety and governance team institution Standards body institution International institution
Infrastructures …infrastructures…
infrastructure Evaluation capacity infrastructure Compute infrastructure Data, language resources, and data labor infrastructure Energy, water, and minerals infrastructure Data work and content moderation Safeguards …and safeguards around AI…
safeguard Risk management process safeguard Impact assessment safeguard Traceability safeguard Audit record safeguard Meaningful human oversight safeguard Supervision and regulatory review safeguard Pre-deployment evaluation and red teaming safeguard Incident reporting and post-deployment monitoring safeguard Complaint and appeal routes safeguard Review and sunset provisions safeguard Compute controls Benefits and harmful patterns …so that its benefits are broadly shared and harmful patterns are addressed…
principle Accountability principle Safety principle Broadly shared benefit pattern Contestable decision pattern Concentration of power pattern Systematically unreliable provenance pattern Over-reliance and deskilling pattern Sycophancy and engagement-driven agreement pattern Homogenisation and loss of authorship Choices and conditions …while they are still choices rather than conditions.
condition Lock-in condition Governance lock-in condition Reversibility window
Principle · Benefits and harmful patterns contested
Broadly shared benefit The positive objective of governance: that access to capability, ownership, returns, and protection from harm are distributed widely rather than captured, without requiring that every benefit be shared equally.
Who receives access, who owns, who captures returns, who supplies labor, who bears costs, and who can contest?
Tests Distribution Legitimacy
Levels National International Recorded relationships Read each statement from its first entity to its second. Status describes our mapping, not certainty of cause.
Data, language resources, and data labor — conditions → Broadly shared benefit
Mapping: asserted
Communities that are data-poor or absent from training data are both more exposed to bias and less able to access benefits.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Governing AI for Humanity: Final Report (opens in a new tab) Executive summary, para. xli (p. 14)
Beyond misuse and missed use of data, the report identifies missing data: large portions of the globe are data poor. Failure to reflect linguistic and cultural diversity has been linked to bias and may also be a missed opportunity for those communities to access AI’s benefits.
Scope note: An advisory body report representing a majority consensus of its members in their personal capacity. It is not a UN member-state decision. Page locators refer to the PDF's printed page numbers.
Passage checked 2026-09-06 · un-hlab-missing-data
International institution — conditions who shapes → Broadly shared benefit
Mapping: contested
Benefits are shaped by the bodies that set terms; when 118 countries are party to none of the sampled initiatives, the distribution of benefit is decided without them.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Governing AI for Humanity: Final Report (opens in a new tab) Executive summary, para. xiv (p. 8); section 3.A 'Representation gaps', paras. 63–65 and figure 8 (pp. 42–43)
Looking at seven prominent non-UN AI governance initiatives, the report finds seven countries party to all of them and 118 countries party to none, primarily in the global South. It calls these representation gaps a problem because the technology is global and comprehensive in its impact.
Scope note: An advisory body report representing a majority consensus of its members in their personal capacity. It is not a UN member-state decision. Page locators refer to the PDF's printed page numbers.
Passage checked 2026-09-06 · un-hlab-representation
Claims and evidence · 6 source-assertion Confidence: high Contestability: low
International AI governance initiatives leave most countries out: seven countries participate in all of seven sampled initiatives while 118 participate in none.
Our reading: The sample is seven non-UN initiatives chosen by the advisory body and counts endorsement of intergovernmental issuances; membership through the EU or African Union alone was not counted.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Governing AI for Humanity: Final Report (opens in a new tab) Executive summary, para. xiv (p. 8); section 3.A 'Representation gaps', paras. 63–65 and figure 8 (pp. 42–43)
Looking at seven prominent non-UN AI governance initiatives, the report finds seven countries party to all of them and 118 countries party to none, primarily in the global South. It calls these representation gaps a problem because the technology is global and comprehensive in its impact.
Scope note: An advisory body report representing a majority consensus of its members in their personal capacity. It is not a UN member-state decision. Page locators refer to the PDF's printed page numbers.
Passage checked 2026-09-06 · un-hlab-representation
source-assertion Confidence: high Contestability: medium
Access to compute, data and talent determines who can participate in AI; the UN advisory body proposes a floor under the AI divide rather than universal access to frontier compute.
Our reading: The 'floor' is a proposal, not an existing arrangement.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Governing AI for Humanity: Final Report (opens in a new tab) Executive summary, section C 'Common benefits', paras. xxxviii–xl (p. 14)
Effective participation depends on access to talent, compute and data. Compute is described as one of the biggest barriers to entry; the report says it is unrealistic to promise access to compute that even the wealthiest countries and companies struggle to acquire, and instead seeks to put a floor under the AI divide.
Scope note: An advisory body report representing a majority consensus of its members in their personal capacity. It is not a UN member-state decision. Page locators refer to the PDF's printed page numbers.
Passage checked 2026-09-06 · un-hlab-compute-floor
source-assertion Confidence: high Contestability: low
Large parts of the world are data-poor, and failure to reflect linguistic and cultural diversity is linked both to bias and to missed benefits.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Governing AI for Humanity: Final Report (opens in a new tab) Executive summary, para. xli (p. 14)
Beyond misuse and missed use of data, the report identifies missing data: large portions of the globe are data poor. Failure to reflect linguistic and cultural diversity has been linked to bias and may also be a missed opportunity for those communities to access AI’s benefits.
Scope note: An advisory body report representing a majority consensus of its members in their personal capacity. It is not a UN member-state decision. Page locators refer to the PDF's printed page numbers.
Passage checked 2026-09-06 · un-hlab-missing-data
source-assertion Confidence: high Contestability: low
India answers compute scarcity with public money: its national AI mission funds a public-private compute pool of 10,000 or more GPUs.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Cabinet approves over Rs 10,300 crore for IndiaAI Mission (opens in a new tab) IndiaAI Compute Capacity, first of seven pillars
The Cabinet approved a national AI mission with an outlay of Rs 10,371.92 crore, whose first pillar builds public-private AI compute of 10,000 or more GPUs.
Scope note: Official release of the Union Cabinet decision. Current GPU counts are reported elsewhere and change over time.
Passage checked 2026-10-09 · indiaai-compute-pillar
source-assertion Confidence: high Contestability: medium
Generative AI exposure falls unevenly: women’s jobs and jobs in richer countries are more exposed, and change within jobs is more likely than outright replacement.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Generative AI and Jobs: A Refined Global Index of Occupational Exposure (opens in a new tab) Abstract and key findings
About one in four workers worldwide is in an occupation with some generative AI exposure. The highest-exposure category covers 4.7% of women’s jobs against 2.4% of men’s, and exposure rises with country income. Transformation of jobs is more likely than replacement.
Scope note: Intergovernmental and tripartite (governments, employers, workers). Measures exposure, not realised job loss.
Passage checked 2026-10-09 · ilo-exposure-uneven
source-assertion Confidence: high Contestability: medium
In a field study at one firm, an AI assistant raised customer-support productivity, most for novice and lower-skilled workers.
Our reading: One company and one task; it measures output, not retained skill. Figures are from the working-paper version.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Generative AI at Work (opens in a new tab) Abstract (NBER working paper w31161)
Access to an AI assistant raised customer-support issues resolved per hour by about 14% on average and by about a third for novice and low-skilled agents, with little effect for the most experienced.
Scope note: Field study of one firm’s customer-support operation. The published abstract was truncated where read; figures come from the open NBER working paper (w31161) and may differ slightly from the final version.
Passage checked 2026-10-18 · brynjolfsson-abstract