An Explainable Feature Selection Approach for Fair Machine Learning

Zhi Yang1; Ziming Wang1; Changwu Huang1; Xin Yao1,2

10.1007/978-3-031-44198-1_7

2023

32nd International Conference on Artificial Neural Networks (ICANN)

0302-9743

1611-3349

978-3-031-44197-4

ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, ICANN 2023, PT VIII

14261

SEP 26-29, 2023

null,Heraklion,GREECE

GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND

SPRINGER INTERNATIONAL PUBLISHING AG

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.

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

第一 ; 通讯

英语

EI ; CPCI-S

National Natural Science Foundation of China[62250710682]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods

WOS:001157297600007

Web of Science

1.Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
2.Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China

Zhi Yang,Ziming Wang,Changwu Huang,et al. An Explainable Feature Selection Approach for Fair Machine Learning[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2023.