Human review is a system design decision
Human-in-the-loop AI assigns specific decisions to accountable reviewers instead of relying on a vague promise that someone will watch the system. The review point should match the consequence and uncertainty of the action.
Low-risk drafts may need sampling, while financial, legal, safety, access, or client-impacting actions may require approval every time.
Design the review experience
A reviewer needs the proposed action, supporting evidence, confidence or uncertainty, applicable policy, and a clear set of choices. Approval without context is only a button, not a control.
- Approve, revise, reject, or escalate
- Record reviewer identity and rationale
- Preserve cited source versions
- Set response deadlines and fallback owners
- Feed overrides into evaluation and improvement
Test the control itself
Measure reviewer agreement, response time, override patterns, missed exceptions, and alert fatigue. A gate that is routinely ignored or rubber-stamped is not reducing risk.
As evidence improves, review can move from every case to risk-based sampling. That change should be approved and documented rather than silently introduced.