AI Governance Checklist
AI governance checklist
A companion reference for reviewing AI systems before they become operating dependencies.
In AI First Principles, an AI governance checklist is not proof that a system is responsible. It is a forcing function for surfacing gaps in ownership, feedback, transparency, agency, and workflow understanding.
The checklist is useful only when failed answers change the system. If every item can be checked without consequence, the checklist has become document theater.
Definition
An AI governance checklist is a set of review questions used before an AI system moves from experiment to operating dependency. It should make assumptions concrete enough to inspect.
The checklist should focus on operational conditions: who owns the outcome, how errors surface, what users can see, what decisions remain human, and what existing workflow logic may be hidden.
AIFP position
The purpose of a checklist is not coverage. It is confrontation with what the team does not yet know.
AIFP uses checklist questions to expose whether the principles have been applied. The questions are not a substitute for the principles.
Review questions
- Ownership: who owns the objective, and do they have authority to stop or change the system?
- Failure: what quiet failure could repeat before anyone notices?
- Transparency: does the affected person know AI is involved?
- Agency: can the user challenge, correct, or exit the AI-shaped path?
- Discovery: which manual step or redundancy might satisfy a requirement nobody documented?
- Judgment: where does the system turn probability into certainty?
- Resources: what cost is hidden in compute, latency, cloud spend, review queues, or attention?
Relevant principles
- People Own Objectives: the checklist must name ownership.
- AI Fails Silently: it must identify feedback and stop conditions.
- Deception Destroys Trust: it must require visible AI involvement.
- Discovery Before Disruption: it must test understanding before removal or automation.
- Ambiguity Is Wisdom: it must identify where uncertainty belongs in the interface or workflow.
Use
Use this checklist at the boundary between prototype and deployment, and again when the system's authority expands. A useful review should produce changes, not just approvals.
If the team cannot answer a question, the next step is discovery, not wording the answer more carefully.
What this is not
- Not a compliance artifact by itself.
- Not evidence that the system is safe.
- Not a replacement for testing, monitoring, user research, or accountability.
- Not useful if no one has authority to act on failed answers.
Related AI First Principles
Related references
AI Governance Framework
A companion reference for applying AI First Principles to governance decisions.
Operationalizing AI
A companion reference for moving AI from experiment into real work without scaling dysfunction.
AI Operating Model
A companion reference for defining how an organization owns, reviews, and changes AI systems.
AI Constitution
A companion reference for using the 12 AI First Principles as organizational constraints.
Start with the 12 principles or read the full treatise.