Conduct an AI feature trust calibration audit

Trust Calibration Is the UX Metric That Actually Matters

AI products can be highly accurate and still fail if users over- or under-rely on the output. Anthropic's writing on AI transparency and Nielsen Norman Group's research on AI UX both argue that trust calibration — whether user acceptance rate matches AI accuracy — is the real UX metric, not raw model quality.

How the Conduct an AI feature trust calibration audit Prompt Works

The prompt audits calibration via behavior signal + 8-10-user testing across four scenarios (high/low confidence × correct/wrong), diagnoses calibration patterns, and matches interventions. The "user segment most likely miscalibrated" output flags the cohort needing the most careful intervention.

When to Use It

• Users are over-relying on AI output (acting without verifying).
• Users are under-relying on AI output (ignoring correct answers).
• A regulated domain requires documented trust calibration.
• A new AI PM is establishing UX metrics.
• A major AI upgrade is landing and calibration needs re-validation.

Common Pitfalls

• Measuring only accuracy. Accuracy without calibration means users misuse even correct outputs.
• Single-scenario testing. Calibration requires testing across confidence × correctness matrix.
• Static calibration. Calibration drifts as model improves or users learn. Re-measure.

Sources

• Anthropic Research — Anthropic
• Usability Testing 101 — Nielsen Norman Group
• Which UX Research Methods — Nielsen Norman Group
• AI Adoption in Product Orgs — Reforge