Fair Without Leveling Down: A New Intersectional Fairness Definition

• URL: http://arxiv.org/abs/2305.12495v2
• Date: Tue, 7 Nov 2023 10:19:02 GMT
• Title: Fair Without Leveling Down: A New Intersectional Fairness Definition
• Authors: Gaurav Maheshwari, Aur\'elien Bellet, Pascal Denis, Mikaela Keller
• Abstract summary: We propose a new definition called the $alpha$-Intersectional Fairness, which combines the absolute and the relative performance across sensitive groups. We benchmark multiple popular in-processing fair machine learning approaches using our new fairness definition and show that they do not achieve any improvement over a simple baseline.
• Score: 1.0958014189747356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we consider the problem of intersectional group fairness in the classification setting, where the objective is to learn discrimination-free models in the presence of several intersecting sensitive groups. First, we illustrate various shortcomings of existing fairness measures commonly used to capture intersectional fairness. Then, we propose a new definition called the $\alpha$-Intersectional Fairness, which combines the absolute and the relative performance across sensitive groups and can be seen as a generalization of the notion of differential fairness. We highlight several desirable properties of the proposed definition and analyze its relation to other fairness measures. Finally, we benchmark multiple popular in-processing fair machine learning approaches using our new fairness definition and show that they do not achieve any improvement over a simple baseline. Our results reveal that the increase in fairness measured by previous definitions hides a "leveling down" effect, i.e., degrading the best performance over groups rather than improving the worst one.

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