Fairness with Overlapping Groups

• URL: http://arxiv.org/abs/2006.13485v1
• Date: Wed, 24 Jun 2020 05:01:10 GMT
• Title: Fairness with Overlapping Groups
• Authors: Forest Yang, Moustapha Cisse, Sanmi Koyejo
• Abstract summary: 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. 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.
• Score: 15.154984899546333
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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