Human-in-the-loop (HITL) keeps people reviewing, correcting, or overriding AI—essential when errors are costly, context is nuanced, or regulations require human decision-makers.
Patterns
- Human-in-the-loop — AI proposes, human approves each case
- Human-on-the-loop — AI acts, human monitors samples and alerts
- Human-out-of-loop — fully automated (only when risk is low and monitored)
Design for reviewers
- Show evidence snippets, not only scores
- Log overrides to improve models and audit fairness
- Limit queue size and SLA to prevent rubber-stamping
- Train reviewers on failure modes and bias
Escalation flow
Low-confidence predictions route to experts; high-confidence routine cases auto-process. Calibrate confidence thresholds on validation data—not guesses.
Important interview questions and answers
- Q: Automation bias?
A: Humans over-trust AI and stop checking—design UI to surface uncertainty. - Q: Override logs?
A: Improve training data and prove accountability during audits.
Self-check
- Contrast HITL vs human-on-the-loop.
- Why log human overrides?
Pitfall: Automation bias—show uncertainty so reviewers do not rubber-stamp.
Interview prep
- Automation bias?
- Humans over-trust AI outputs and stop critical review.
- Override logs?
- Improve models and demonstrate accountability during audits.