feat(ai-206): add policy-aware score calibration for AI appeal outputs#1
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khaadish wants to merge 1 commit into
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feat(ai-206): add policy-aware score calibration for AI appeal outputs#1khaadish wants to merge 1 commit into
khaadish wants to merge 1 commit into
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- ScoreCalibrationService decouples raw model confidence from policy thresholds - CalibrationPolicy exposes approveThreshold, rejectThreshold, humanReviewThreshold, biasCorrectionFactor - needsHumanReview=true whenever confidence < humanReviewThreshold (explicit review boundary) - appliedPolicy + rawScore preserved in every output for audit/replay - Corresponding types exported from @devconsole/api-contracts - Spec covers approve/escalate/reject bands, bias correction, clamping, and traceability Closes Ibinola#423
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Summary
Separates raw model confidence from policy thresholds so fairness, review timing, and risk tolerances can be tuned without rewriting the AI pipeline.
Changes
ScoreCalibrationService— explicit inputs (rawScore,CalibrationPolicy) and outputs (band,action,needsHumanReview,appliedPolicy)needsHumanReview=truewheneverconfidence < humanReviewThreshold— hard review boundarybiasCorrectionFactorenables fairness tuning without touching model code@devconsole/api-contractsdocs/ai-206-score-calibration.mdAcceptance Criteria
CalibrationPolicy— measurable and safe to tuneneedsHumanReview+appliedPolicyenable human inspection and audit replayCloses Ibinola#423