Bias Monitoring & Fairness
What This Requires
Monitor for bias in AI system outputs across protected attributes (race, gender, age). Measure fairness metrics (demographic parity, equalized odds) and alert on disparities. Conduct bias audit annually.
Why It Matters
Biased AI perpetuates discrimination and violates regulations (GDPR, EEOC). Continuous monitoring detects bias before harm occurs.
How To Implement
Identify Protected Attributes
Determine which attributes to monitor (race, gender, age, disability). Collect demographic data only if legally permissible and with consent.
Choose Fairness Metrics
Select metrics: demographic parity (equal positive rate across groups), equalized odds (equal TPR/FPR across groups), calibration (predicted probability = actual probability). Tailor to use case.
Measure & Alert
Calculate metrics weekly. Alert if disparity exceeds threshold (e.g., >10% difference in positive rate). Investigate root cause (biased training data, flawed features).
Annual Audit
Conduct comprehensive bias audit: test across all demographics, review historical trends, document findings and remediation. Engage external auditor if high-risk.
Evidence & Audit
- Bias monitoring implementation (code, config)
- Fairness metric definitions and thresholds
- Demographic data collection and consent procedures
- Alert history and investigation records
- Annual bias audit reports