Require calibrated evaluation and human review | AIGP
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Require calibrated evaluation and human review: Which procurement condition best addresses the immediate

AIGP Understanding the Foundations of AI Governance Easy

Probabilistic outputs require calibration evidence and oversight where uncertainty or unusual cases remains material.

The question

An insurer pilots a claim-triage tool that assigns probabilities rather than definitive outcomes. Claims staff may use the scores, but the pilot lacks evidence about calibration for unusual claims. Which procurement condition best addresses the immediate governance concern?

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  1. Require staff to treat every score as a final decision.
    Treating probabilities as final decisions removes appropriate oversight despite incomplete calibration evidence for unusual claims.
  2. Require calibrated evaluation and human review for uncertain claims. ✓
    Calibration testing addresses probability quality, while review manages uncertainty where evidence is incomplete or unusual.
  3. Replace the tool with a generative model for claim summaries.
    Changing output format does not address whether probability estimates are reliable for unusual claims.
  4. Approve deployment after measuring accuracy on ordinary claims.
    Ordinary-claim accuracy alone does not establish reliable probabilities for the unusual cases identified in the pilot.
The trap
Look for uncertainty evidence, not merely a single accuracy result.

How to remember it

Probabilistic outputs require calibration evidence and oversight where uncertainty or unusual cases remains material.

How many of these would you get right?

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