5.5 — Human Review & Confidence Calibration

CCAR-F Domain 5 · Task 5.5

What You Need to Know

The Aggregate Metrics Trap

97% overall accuracy can hide catastrophic failure rates on specific document types or fields (e.g. handwritten receipts at 60%). Always validate by document type and field segment before automating.

Stratified Random Sampling

Sample high-confidence extractions as well as low-confidence ones. High-confidence items are automated; only stratified sampling detects novel error patterns on them.

Field-Level Confidence Calibration

Raw model confidence is uncalibrated. Calibrate against labelled validation sets (ground truth). Build calibration curves per field type per document type. Then set thresholds for automation vs human review.

Reviewer Capacity Prioritisation

Route highest-uncertainty items first. Do not spread capacity evenly. Dynamic priority queue ordered by uncertainty.

Validation Sequence Before Automation

1. Measure accuracy by document type and field segment
2. Calibrate confidence scores on labelled sets
3. Set calibrated thresholds
4. Implement stratified random sampling
5. Only then reduce human review on validated segments

Key Concept: 97% aggregate can hide 40% error rates on specific types. Validate by segment, calibrate against ground truth, sample high-confidence items, prioritise highest-uncertainty.

Exam Traps

• Using aggregate accuracy to justify automating all high-confidence extractions
• Only sampling low-confidence extractions
• Using raw confidence scores without calibration
• Spreading reviewer capacity evenly