Fairness in Machine Learning: Metrics, Trade-offs, and Evaluation
Fairness is not a property that can be established by checking whether one metric exceeds a universal threshold. It is a system-level question about people, decisions, benefits, harms, institutions, and the evidence used to justify an intervention. Machine-learning metrics still matter. They can reveal differences in selection rates, error rates, calibration, and performance across groups. But choosing a metric is already a policy decision: it determines which differences count as harms and which trade-offs receive attention. ...