Production Fairness Architecture and Monitoring

Fairness Pipeline Architecture

Build fairness into your ML pipeline, not as an afterthought. Data layer: Track demographic distribution in training data. Alert if representation drops below threshold (e.g., Group B below 15% of data). Training layer: Compute fairness metrics on validation set during training. If demographic parity ratio drops below 0.8, trigger alert. Serving layer: Log predictions with demographic attributes to separate audit table. Never store demographics in main prediction path. Monitoring layer: Dashboard showing fairness metrics over time, sliced by deployment region and model version.

Continuous Monitoring Setup

Fairness can drift. A model fair at deployment may become unfair as user population changes. Monitor: Demographic parity ratio: Alert if falls below 0.8. Equalized odds difference: Alert if exceeds 0.1 (10 percentage points). Calibration by group: Plot weekly reliability diagrams. Feature drift by group: If Group B feature distributions shift more than Group A, investigate. Typical cadence: daily automated checks, weekly manual review, monthly fairness audit report.

Incident Response

When fairness alerts trigger: Severity 1 (legal risk): Model showing clear discrimination (demographic parity below 0.5). Immediate rollback to previous version. Severity 2 (degradation): Fairness metrics declining but above threshold. Investigate root cause within 48 hours. Severity 3 (monitoring gap): Cannot compute fairness metrics due to missing demographics. Not immediately actionable but must fix data collection. Document all incidents in fairness incident log for regulatory audits.