Lesson 194 of 1550
Bias Audits That Catch Problems Before Deployment: A Production Audit Pipeline
Bias audits run once at deployment miss everything that emerges in production — distribution shift, edge-case interactions, fairness drift. A real audit pipeline runs continuously and surfaces issues to humans for evaluation.
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What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2bias audit
- 3fairness metrics
- 4disparate impact
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Section 1
The premise
Bias audits at deployment catch only what was tested; production audits catch what emerges with real users.
What AI does well here
- Define fairness metrics appropriate to the use case (demographic parity, equal opportunity, calibration) before launch
- Implement automated audits running on production traffic with alerting on drift
- Maintain a fairness incident process — what happens when an audit flags a problem
- Document the protected attributes and proxies the system might be using
What AI cannot do
- Resolve the trade-offs between competing fairness metrics (no single metric satisfies all)
- Replace human review of borderline fairness cases
- Substitute for the diverse stakeholder input that defines what 'fair' means in context
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