The short answer
Review AI approval quality by defining the complete eligible population, stratifying by action and risk, and sampling approvals, rejections, edits, expirations, overrides, and bypasses. For each item, test evidence sufficiency, reviewer authority, decision timing, modification after approval, external outcome, and whether the record supports the rationale.
Operational checklist
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Key takeaways
Construct the denominator before drawing a sample
Record the review period, workflow and policy versions, eligible action classes, properties/entities, generated proposals, items never queued, queued items, approvals, rejections, edits, expirations, overrides, withdrawn items, and executed outcomes. Reconcile counts and deduplicate retries.
Choose sample strata based on consequence and known failure modes rather than selecting only easy completed approvals. Include new reviewers, high-risk actions, low-confidence or conflicting evidence, large edits, fast approvals, aged items, and unusual outcomes.
Score the review record, not the reviewer’s intent
The rubric should be observable and linked to the policy in effect at decision time. The quality team should not substitute hindsight for the evidence that was actually available.
| Dimension | Pass evidence | Failure signal |
|---|---|---|
| Eligibility | Correct action reached the required gate | Bypass or wrong risk class |
| Evidence | Required source, freshness, and conflicts visible | Summary hides missing or contradictory source |
| Authority | Reviewer had scope and limit | Self-approval or expired role |
| Decision | Approval/rejection/edit tied to the exact version | Material change after approval |
| Execution | External effect matches approved action once | Duplicate, wrong recipient/property, or drift |
| Closure | Receipt and exception state reconciled | Approval treated as outcome proof |
Turn findings into bounded control changes
Classify findings by failure mode and consequence, quantify them only against the defined sample and population, and avoid presenting a small convenience sample as a performance claim. Assign changes to interface, evidence retrieval, policy, reviewer training, permissions, queue design, or deterministic validation as appropriate.
Re-test changed controls and preserve the original finding. If the sample discovers an active consequential defect, open an incident and expand the affected population instead of waiting for the periodic report.
Edge cases
Sources and references
Follow each source to check the underlying claim. Access checks and professional review are different steps.