AI Output Risk, Human Review & Escalation Readiness

The likely issue is a contract mismatch around AI output risk, confidence/uncertainty signals, harmful or high-impact errors, human review, escalation routes, user messaging, audit evidence, overrides, and operational ownership. I would first define the invariant: Automation depth must be proportional to consequence and evidence: high-impact or materially uncertain outputs require the documented human/policy owner before an irreversible or externally committed action occurs. Then I would trace the smallest owning boundary, apply or propose a bounded repair/review step, and add deterministic normal, boundary, failure and replay tests. The workflow preserves existing product/safety policy, human decision ownership, legal/compliance authority, privacy, and user communication commitments. If repository/workspace files are available, the findings can be mapped to exact paths and focused local validation; with partial evidence, the result stays an explicit diagnosis/evidence map with assumptions and no invented execution claims.