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
Pattern · Benefits and harmful patterns emerging
Systematically unreliable provenance An information environment in which the origin and authenticity of content can no longer be established at scale, so that individual misleading items become a condition rather than incidents.
Is the problem a piece of content, or an environment in which no one can tell?
Tests Distribution Legitimacy
Lifecycle Deployment Monitoring Recorded relationships Read each statement from its first entity to its second. Status describes our mapping, not certainty of cause.
Audit record — mitigates → Systematically unreliable provenance
Mapping: hypothesized
Provenance records for data and outputs are the safeguard aimed at an environment in which disinformation can be created and disseminated at scale.
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. iii (p. 7)
AI bias and surveillance are joined by newer concerns including confabulations of large language models, AI-enhanced creation and dissemination of disinformation, risks to peace and security, and the energy consumption of AI systems at a time of climate crisis.
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-risks
Claims and evidence · 1 source-assertion Confidence: high Contestability: low
AI-enhanced creation and dissemination of disinformation is identified among AI's risks alongside bias, surveillance, and energy consumption.
Our reading: The report names the risk; it does not measure the scale of provenance failure, which remains an empirical question.
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. iii (p. 7)
AI bias and surveillance are joined by newer concerns including confabulations of large language models, AI-enhanced creation and dissemination of disinformation, risks to peace and security, and the energy consumption of AI systems at a time of climate crisis.
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-risks