Jev confidence thresholds and human review
Design risk-aware Jev confidence thresholds, calibration tests, and human-review bands instead of relying on one global cutoff.
1. Confidence is not probability
For Choice and Score, confidence summarizes how decisive the full distribution is. A winning option can have the highest probability while the distribution is still ambiguous.
2. Use risk bands
Define three outcomes: automate, request review, or abstain. The boundary should be stricter for refunds, account changes, deletion, and other irreversible actions than for reversible routing suggestions.
# Illustrative policy — calibrate these values on your data.
if answer.confidence >= AUTO_THRESHOLD:
apply_reversible_action(answer.choice)
elif answer.confidence >= REVIEW_THRESHOLD:
enqueue_human_review(answer)
else:
abstain_and_request_more_context()3. Calibrate with your own data
Collect representative labeled cases, run the exact production questions, group results into confidence bands, and compare observed error rates. Recheck after changing models, criteria, or input structure.
4. Log the decision contract
Store question version, model version, selected answer, distribution, confidence, threshold, and final action. This makes regressions and review overrides measurable.