Human-in-the-loop design works when the system knows which decisions need review, provides the right context and learns from the outcome without turning people into permanent middleware.
When should an AI system ask for review?
Escalate based on consequence, uncertainty and novelty—not on a vague sense that AI might be wrong.
Low-confidence classification may be harmless when it only sorts an internal queue. The same confidence is unacceptable when it changes a customer record or approves a payment.
- The action is difficult to reverse
- The financial or legal consequence is material
- The input is outside known patterns
- Required evidence is missing
- Policies conflict
Make review fast and informed
The reviewer needs the recommendation, source evidence, confidence and available actions in one place.
A review queue that forces people to reconstruct context manually removes most of the value. Good interfaces compress the decision without hiding its basis.
Use outcomes as operational data
Approved, edited and rejected actions reveal where the system needs better rules, context or model behaviour.
Capture the reason for overrides. Patterns in those reasons are more useful than a single overall accuracy number.