Confidence is not truth
Explain why confidence scores require calibration and consequence-aware thresholds before they drive human review.
The useful question is not 'What is the score?' It is 'What does this score mean in this workflow?' A confidence score can support human review only after it has been interpreted. First, check calibration: when the model says 70%, is it right about 70% of the time for this case type? Second, check segmentation: does the score behave the same for new customers, older records, rare policies, or messy inputs? Third, check consequence: what happens if the accepted prediction is wrong? This is why a HITL threshold should be written like a policy, not a magic number. Example: 'Auto-approve…
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