Confidence Calibration & Review Thresholds

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

Calibration Process

1. Have models output field-level confidence scores alongside extractions
2. Collect a labeled validation set with known-correct values
3. Compare model confidence to actual accuracy across the validation set
4. Set review thresholds where confidence correlates with acceptable accuracy

Why Calibration Matters

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

Segment-Level Validation

Before automating high-confidence extractions:

1. Validate accuracy by document type AND field
2. Verify consistent performance across all segments
3. Only reduce human review for segments with proven accuracy
4. Continue sampling even automated segments for drift detection

Progressive Automation

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