Fairness with Overlapping Groups

Abstract

In algorithmically fair prediction problems, a standard goal is to ensure the equality of fairness metrics across multiple overlapping groups simultaneously. We reconsider this standard fair classification problem using a probabilistic population analysis, which, in turn, reveals the Bayes-optimal classifier. Our approach unifies a variety of existing group-fair classification methods and enables extensions to a wide range of non-decomposable multiclass performance metrics and fairness measures. The Bayes-optimal classifier further inspires consistent procedures for algorithmically fair classification with overlapping groups. On a variety of real datasets, the proposed approach outperforms baselines in terms of its fairness-performance tradeoff.

References in corpus (3)

• Equality of Opportunity in Supervised Learning
• Convex Calibration Dimension for Multiclass Loss Matrices
• Consistent Classification with Generalized Metrics

Cited by in corpus (4)

• Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation
• A survey on datasets for fairness-aware machine learning
• Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity
• Optimizing Black-box Metrics with Iterative Example Weighting