Turn AI ethics research into reproducible compliance evidence.
Compliance evaluation should not stop at policy statements. It needs repeatable scenarios, observable behavior, risk thresholds, and review decisions that can survive audit pressure.
Scenario
Define the environment, actors, incentives, constraints, and failure modes before
judging the model.
Evidence
Capture quantitative traces, qualitative review notes, and the exact conditions that
produced the behavior.
Threshold
Set escalation rules for welfare loss, unfair allocation, unstable cooperation, and
unexpected policy drift.
Decision
Record whether the model is approved, limited, monitored, retrained, or blocked from a
deployment path.
Recommended audit questions
- Can the evaluation be reproduced with the same scenario parameters?
- Does the test include stress conditions, not only normal cases?
- Which stakeholder group bears the downside when optimization succeeds on average?
- What monitoring signal would show that the deployed model has drifted?