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
Safety The value that AI systems should not cause serious harm through failure, misuse, or loss of control. Which harms count, and whose, is contested.
Safe from what, for whom, and on what evidence, before the principle becomes a safeguard?
Tests Power Distribution Recorded relationships Read each statement from its first entity to its second. Status describes our mapping, not certainty of cause.
Safety — operationalized by → Pre-deployment evaluation and red teaming
Mapping: asserted
'AI should be safe' is a principle; testing against specified capabilities before deployment is a safeguard with an actor and a trigger.
Inspect evidence · source links · passage review pending Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Compute controls — intended to protect → Safety
Mapping: asserted
The stated purpose of compute governance is regulatory visibility and enforcement against irresponsible or malicious development.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Computing Power and the Governance of Artificial Intelligence (opens in a new tab) Abstract
Relative to data and algorithms, AI-relevant compute is presented as a particularly effective point of intervention because it is detectable, excludable and quantifiable and is produced via an extremely concentrated supply chain. Policymakers could use it for regulatory visibility, resource allocation and enforcement; the authors warn that naive or poorly scoped compute governance carries significant risks to privacy, economic impacts and centralization of power, and suggest guardrails.
Scope note: A preprint by authors affiliated with AI developers, universities, and governance organizations; not peer reviewed. Several authors have institutional stakes in compute governance.
Passage checked 2026-09-06 · sastry-compute-abstract
Claims and evidence · 3 synthesis Confidence: high Contestability: low
AI risk management is an ongoing organizational practice across the system lifecycle, not a one-time approval gate.
Inspect evidence · source links · passage review pending Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
interpretation Confidence: medium Contestability: medium
A principle states a value; in this framework, a safeguard is a specified mechanism with an actor, a trigger, a record and a consequence. Specifying the mechanism lets us inspect how the value reaches a decision.
Our reading: AIGovSpec's analytical distinction, not a universal definition or evidence that mechanisms alone are sufficient. These sources connect values to organizational arrangements and challenge routes; they do not establish our four-part definition as a consensus.
Inspect evidence · 3 passages Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law (CETS No. 225) (opens in a new tab) Overview page — introduction; 'What does the Framework Convention require states to do?': fundamental principles; remedies, procedural rights and safeguards
The Council of Europe describes the Convention as the first international legally binding treaty in the field, opened for signature on 5 September 2024, covering activities within the lifecycle of AI systems. It lists principles including transparency and oversight and accountability and responsibility, and says parties must ensure documentation sufficient for affected people to challenge decisions and the use of a system, with an effective possibility to complain to competent authorities.
Scope note: The Council of Europe overview page was reviewed, not the treaty text itself. The Convention binds only parties for which it has entered into force, and is largely framed as obligations on states to adopt measures. Signature and ratification status changes; verify before relying on it. The treaty text itself (CETS 225) was opened on 19 October 2026 for the definitions comparison.
Passage checked 2026-09-06 · coe-principles-remedies
source-assertion Confidence: high Contestability: low
UNICEF’s 2025 guidance on AI and children says children’s safety must be assured over both the short and the long term.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Guidance on AI and children 3.0 (opens in a new tab) Requirement 2, “Ensure safety for children” (about p. 15)
Children’s safety in relation to AI systems must be assured in both the short and the long term.
Scope note: Supersedes the 2021 Policy guidance on AI for children (2.0). Ten requirements for child-centred AI; guidance, not law. Page numbers below are from the PDF as extracted.
Passage checked 2026-10-18 · unicef-safety-requirement