An Explainable Feature Selection Approach for Fair Machine Learning

Zhi YANG, Ziming WANG, Changwu HUANG*, Xin YAO

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

1 Citation (Scopus)


As machine learning (ML) algorithms are extensively adopted in various fields to make decisions of importance to human beings and our society, the fairness issue in algorithm decision-making has been widely studied. To mitigate unfairness in ML, many techniques have been proposed, including pre-processing, in-processing, and post-processing approaches. In this work, we propose an explainable feature selection (ExFS) method to improve the fairness of ML by recursively eliminating features that contribute to unfairness based on the feature attribution explanations of the model’s predictions. To validate the effectiveness of our proposed ExFS method, we compare our approach with other fairness-aware feature selection methods on several commonly used datasets. The experimental results show that ExFS can effectively improve fairness by recursively dropping some features that contribute to unfairness. The ExFS method generally outperforms the compared filter-based feature selection methods in terms of fairness and achieves comparable results to the compared wrapper-based feature selection methods. In addition, our method can provide explanations for the rationale underlying this fairness-aware feature selection mechanism. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning : ICANN 2023 : 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, September 26–29, 2023, Proceedings, Part VIII
EditorsLazaros ILIADIS, Antonios PAPALEONIDAS, Plamen ANGELOV, Chrisina JAYNE
Number of pages12
ISBN (Electronic)9783031441981
ISBN (Print)9783031441974
Publication statusPublished - 2023
Externally publishedYes
Event32nd International Conference on Artificial Neural Networks - Heraklion, Crete, Greece
Duration: 26 Sept 202329 Sept 2023

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference32nd International Conference on Artificial Neural Networks
Abbreviated titleICANN 2023

Bibliographical note

This work was supported by the National Natural Science Foundation of China (Grant No. 62250710682), the Guangdong Provincial Key Laboratory (Grant No. 2020B121201001), the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (Grant No.2017ZT07X386), the Shenzhen Science and Technology Program (Grant No. KQTD2016112514355531), and the Research Institute of Trustworthy Autonomous Systems.


  • Ethics of AI
  • Fairness in machine learning
  • Feature attribution explanation
  • Feature selection
  • Group fairness


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