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
Infrastructure · Infrastructures emerging
Evaluation capacity The people, expertise, access, compute, data, and institutions needed to test governance claims.
Who has the practical means to verify what a system can do and whom it affects?
Tests Power Distribution
Lifecycle Evaluation Monitoring
Levels National Corporate Recorded relationships Read each statement from its first entity to its second. Status describes our mapping, not certainty of cause.
Evaluation capacity — enables → Supervision and regulatory review
Mapping: contested
Formal authority without technical and institutional capacity may not produce effective oversight.
Inspect evidence · source links · passage review pending Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Evaluation capacity — enables → Pre-deployment evaluation and red teaming
Mapping: asserted
Evaluation requires people, access, compute and data; where those are scarce the safeguard exists on paper only.
Inspect evidence · 1 passage · passage review pending 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
Claims and evidence · 5 interpretation Confidence: medium Contestability: high
Access to data, compute, expertise, and evaluation capacity shapes which actors can develop systems and which institutions can govern them effectively.
Our reading: This cross-cutting claim is intentionally marked as interpretation pending a broader evidence review.
Inspect evidence · source links · passage review pending Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
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
An Africa-led global index finds Global South AI governance relies mostly on non-binding frameworks, leaving a wide North–South gap in responsible-AI capacity.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Global Index on Responsible AI, 2026 Report (opens in a new tab) Global South findings: “Responsible AI governance is expanding but binding protections remain weak”; Equity
78% of responsible-AI framework cases in Global South countries are non-binding, against 42% in the Global North; average index scores are 27 and 55 respectively.
Scope note: Africa-led research index covering 135 countries and jurisdictions. Its scores reflect its own methodology.
Passage checked 2026-10-09 · girai-non-binding-gap
source-assertion Confidence: medium Contestability: high
One observational study found that endoscopists detected fewer precancerous growths in colonoscopies done without AI after AI assistance became routine at their centres.
Our reading: A single before-and-after study at four centres in one country, with possible confounding; it has drawn published correspondence (not read here) and awaits replication. It is a signal, not proof of deskilling in general.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational study (opens in a new tab) Abstract, Findings and Interpretation
At four Polish centres, the adenoma detection rate in colonoscopies done without AI fell from 28.4% to 22.4% in the three months after AI assistance was introduced, compared with the three months before. The authors say continuous AI exposure might reduce performance without it.
Scope note: Paywalled; the abstract was read through Europe PMC (PMID 40816301). Observational before-and-after comparison at four centres in Poland. The paper received published correspondence and an erratum, which were not read.
Passage checked 2026-10-18 · budzyn-abstract
source-assertion Confidence: high Contestability: medium
UNESCO’s 2023 guidance recommends thirteen as the minimum age for independent conversations with generative AI and strict validation of tools before schools or universities officially adopt them.
Our reading: The guidance itself notes that some consider thirteen too young. Validation requires capacity many institutions lack.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Guidance for generative AI in education and research (opens in a new tab) Section 3.3.1 (p. 21) and section 4.3 (p. 25)
The guidance recommends thirteen as the minimum age for independent conversations with generative AI, noting that some consider this too young, and calls for strict ethical and pedagogical validation of tools before institutions officially adopt them.
Scope note: The UNESCO repository page did not render; the passage was read in an open copy held by University College London.
Passage checked 2026-10-18 · unesco-age-and-validation