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
Safeguard · Safeguards contested
Impact assessment A structured inquiry into an AI system's context, affected groups, plausible impacts, and proposed mitigations.
What could change, for whom, and what evidence would justify proceeding?
Tests Distribution Legitimacy
Lifecycle Procurement Deployment Recorded relationships Read each statement from its first entity to its second. Status describes our mapping, not certainty of cause.
Impact assessment — should account for → Affected people
Mapping: contested
Impact assessment changes meaning depending on whether affected people supply evidence, exercise rights, or remain abstract risk categories.
Inspect evidence · source links · passage review pending Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Claims and evidence · 4 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.
source-assertion Confidence: high Contestability: medium
Human rights and dignity can function as the normative foundation against which AI governance choices are evaluated.
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: low
Canada makes algorithmic impact assessment mandatory for federal automated decisions, scales required mitigations to the assessed impact level, and requires the results to be published.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.
Algorithmic Impact Assessment tool (opens in a new tab) Overview and publication sections
The Algorithmic Impact Assessment is mandatory under the directive; its impact level sets the required mitigations, and departments must publish the final results in an accessible format.
Scope note: Official guidance for the Treasury Board Directive on Automated Decision-Making (amended June 2025). The directive text on tbs-sct.canada.ca could not be retrieved for this check; section numbers are not cited.
Passage checked 2026-10-09 · canada-aia-publication
source-assertion Confidence: high Contestability: low
Public consultation inside an impact assessment becomes a formality unless it comes with explicit requirements.
Inspect evidence · 1 passage Passage summaries are paraphrases, not quotations. Open the source to read the full passage.