Fair Without Leveling Down: A New Intersectional Fairness Definition

• Published in: arXiv.org
• Main Authors: Maheshwari, Gaurav, Bellet, Aurélien, Pascal, Denis, Keller, Mikaela
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 07.11.2023
• Subjects: Performance degradation

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.