Confidence Calibration & Review Thresholds

Advanced

Design human review workflows and confidence calibration · Difficulty 4/5

confidencecalibrationvalidationautomation

Prerequisites

Field-level confidence scores enable intelligent routing of review attention, but only when properly calibrated against labeled validation sets.

Have models output field-level confidence scores alongside extractions

Collect a labeled validation set with known-correct values

Compare model confidence to actual accuracy across the validation set

Set review thresholds where confidence correlates with acceptable accuracy

Uncalibrated confidence scores are unreliable:

Model may report high confidence on systematically wrong extractions

Confidence distribution may not match actual error distribution

Without calibration, threshold-based routing produces unpredictable results

Before automating high-confidence extractions:

Validate accuracy by document type AND field

Verify consistent performance across all segments

Only reduce human review for segments with proven accuracy

Continue sampling even automated segments for drift detection

Start with 100% human review, then progressively reduce based on validated confidence calibration per segment -- not based on aggregate metrics.

Aggregate accuracy metrics can mask poor performance on specific segments

Escalate for genuine policy gaps, not just complexity