Design the decision policy for a high-stakes classifier. When do you auto-approve, auto-reject, or send to human review?
formulate your answer, then —
tldr
Human-in-the-loop systems use calibrated risk bands, not one threshold. Auto-decide low/high confidence cases and route uncertain or high-impact cases to review. Design reviewer quality, capacity, audit sampling, active learning, and feedback-loop controls as part of the ML system.
follow-up
- How do you choose thresholds when human review capacity is limited?
- Why can active learning bias your evaluation set?
- How do you measure reviewer label quality?