@inproceedings{maheshwari-etal-2023-fair,
title = "Fair Without Leveling Down: A New Intersectional Fairness Definition",
author = "Maheshwari, Gaurav and
Bellet, Aur{\'e}lien and
Denis, Pascal and
Keller, Mikaela",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.558",
doi = "10.18653/v1/2023.emnlp-main.558",
pages = "9018--9032",
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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<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 α-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.</abstract>
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%0 Conference Proceedings
%T Fair Without Leveling Down: A New Intersectional Fairness Definition
%A Maheshwari, Gaurav
%A Bellet, Aurélien
%A Denis, Pascal
%A Keller, Mikaela
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F maheshwari-etal-2023-fair
%X 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 α-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.
%R 10.18653/v1/2023.emnlp-main.558
%U https://aclanthology.org/2023.emnlp-main.558
%U https://doi.org/10.18653/v1/2023.emnlp-main.558
%P 9018-9032
Markdown (Informal)
[Fair Without Leveling Down: A New Intersectional Fairness Definition](https://aclanthology.org/2023.emnlp-main.558) (Maheshwari et al., EMNLP 2023)
ACL