The original title is about human-in-the-loop AI design, specifically about decision boundaries between model and human decisions.

Short summary

Human-in-the-loop design for AI products requires placing human review at the right decision boundary, not everywhere. Three factors define that boundary: cost of error, reversibility of the action, and model confidence. The article provides a Python calibration function using scikit-learn to find the optimal confidence threshold that keeps autonomous-zone errors below a business-defined maximum while minimizing escalation volume. A worked calibration table shows how threshold 0.85 yields 1.4% error rate with 41% escalation on 10k decisions.

• •HITL boundary determined by cost of error, reversibility, and model confidence
• •Confidence thresholds must be calibrated per task type — uncalibrated thresholds are worse than none
• •Python code finds optimal threshold balancing error pass-through against reviewer workload

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