Fair Feature Selection: A Causal Perspective
Authors: 
Zhaolong Ling, Enqi Xu, Peng Zhou, Liang Du, Kui Yu, 
Xindong WuAuthors Info & Claims
Zhaolong Ling, Enqi Xu, Peng Zhou, Liang Du, Kui Yu, Xindong WuAuthors Info & ClaimsArticle No.: 163, Pages 1 - 23
Abstract
Fair feature selection for classification decision tasks has recently garnered significant attention from researchers. However, existing fair feature selection algorithms fall short of providing a full explanation of the causal relationship between features and sensitive attributes, potentially impacting the accuracy of fair feature identification. To address this issue, we propose a fair causal feature selection algorithm, called FairCFS. Specifically, FairCFS constructs a localized causal graph that identifies the Markov blankets of class and sensitive variables, to block the transmission of sensitive information for selecting fair causal features. Extensive experiments on seven public real-world datasets validate that FairCFS has accuracy comparable to eight state-of-the-art feature selection algorithms while presenting more superior fairness.
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Index Terms
- Fair Feature Selection: A Causal Perspective
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Publication History
Published: 19 June 2024
Online AM: 03 February 2024
Accepted: 30 January 2024
Revised: 09 January 2024
Received: 15 September 2023
Published in TKDD Volume 18, Issue 7
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- National Key Research and Development Program of China
- National Natural Science Foundation of China
- Natural Science Project of Anhui Provincial Education Department
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