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ML Fairness and Bias: Metrics, Trade-offs, and Mitigation Strategies
Fairness in ML systems is a first-class engineering problem, not just a policy concern. This guide covers the four main fairness definitions (demographic parity, equalized odds, calibration, individual fairness), their mathematical incompatibility, bias sources across the ML pipeline, and practical mitigation strategies — tested increasingly at Google, Meta, Microsoft, and AI-first companies in senior ML system design rounds.
Why ML Fairness Is an Engineering Problem
An ML model that achieves 92% overall accuracy can still systematically disadvantage a demographic group — producing higher false positive rates for loan denials, lower recall for medical diagnoses, or biased rankings in hiring tools. These are not edge cases: they are structural outcomes of how models are trained on historical data that reflects past discrimination.
Fairness is an engineering problem because:
- Bias enters through data, not just model design: historical data contains human biases that the model learns and amplifies
- Fairness metrics conflict mathematically: you cannot simultaneously satisfy demographic parity, equalized odds, and calibration — Chouldechova (2017) proved this formally
- The choice of fairness metric is a policy decision with legal implications: GDPR, the Equal Credit Opportunity Act, and EEOC guidelines create legal requirements that translate directly to metric choices
Senior ML engineers at FAANG and AI-first companies are expected to:
- Know the main fairness metrics and their definitions
- Know which metrics conflict and when
- Know where bias enters the ML pipeline
- Propose concrete mitigation strategies at each stage