Fairness May Backfire: When Leveling-Down Occurs in Fair Machine Learning
As machine learning (ML) systems increasingly shape access to credit, jobs, and other opportunities, the fairness of algorithmic decisions has become a central concern. Yet it remains unclear when enforcing fairness constraints in these systems genuinely improves outcomes for affected groups or instead leads to "leveling down," making one or both groups worse off. We address this question in a unified, population-level (Bayes) framework for binary classification under prevalent group fairness notions. Our Bayes approach is distribution-free and algorithm-agnostic, isolating the intrinsic effect of fairness requirements from finite-sample noise and from training and intervention specifics....
| Authors | Yi Yang, Xiangyu Chang, Pei-yu Chen |
|---|---|
| Primary category | stat.ML |
| Published | 6 March 2026 |
| Last updated at source | 6 March 2026 |
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