Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Angwin, J., Larson, J., Mattu, S., Kirchner, L.: Machine bias (2016). https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
Arrieta, A.B., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82–115 (2020)
Dorleon, G., Megdiche, I., Bricon-Souf, N., Teste, O.: Feature selection under fairness constraints. In: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, pp. 1125–1127 (2022)
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.: Fairness through awareness. In: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, pp. 214–226 (2012)
Gajane, P., Pechenizkiy, M.: On formalizing fairness in prediction with machine learning. arXiv preprint arXiv:1710.03184 (2017)
Grgic-Hlaca, N., Zafar, M.B., Gummadi, K.P., Weller, A.: The case for process fairness in learning: feature selection for fair decision making. In: NIPS Symposium on Machine Learning and the Law, Barcelona, Spain, vol. 1, p. 11 (2016)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)
Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. In: Advances in Neural Information Processing Systems, vol. 29 (2016)
Huang, C., Zhang, Z., Mao, B., Yao, X.: An overview of artificial intelligence ethics. IEEE Trans. Artif. Intell. 1–21 (2022). https://doi.org/10.1109/TAI.2022.3194503
Khodadadian, S., Nafea, M., Ghassami, A., Kiyavash, N.: Information theoretic measures for fairness-aware feature selection. arXiv preprint arXiv:2106.00772 (2021)
Le Quy, T., Roy, A., Iosifidis, V., Zhang, W., Ntoutsi, E.: A survey on datasets for fairness-aware machine learning. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 12(3), 1–59 (2022)
Lou, Y., Caruana, R., Gehrke, J., Hooker, G.: Accurate intelligible models with pairwise interactions. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, pp. 623–631. Association for Computing Machinery, New York (2013)
Lundberg, S.M.: Explaining quantitative measures of fairness. In: Fair & Responsible AI Workshop@ CHI2020 (2020)
Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning. ACM Comput. Surv. (CSUR) 54(6), 1–35 (2021)
Nori, H., Jenkins, S., Koch, P., Caruana, R.: InterpretML: a unified framework for machine learning interpretability. arXiv preprint arXiv:1909.09223 (2019)
Pessach, D., Shmueli, E.: A review on fairness in machine learning. ACM Comput. Surv. (CSUR) 55(3), 1–44 (2022)
Rehman, A.U., Nadeem, A., Malik, M.Z.: Fair feature subset selection using multiobjective genetic algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 360–363 (2022)
Ribeiro, M.T., Singh, S., Guestrin, C.: “Why should I trust you?”: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144. Association for Computing Machinery, New York (2016)
Singh, M.: Fair classification under covariate shift and missing protected attribute-an investigation using related features. arXiv preprint arXiv:2204.07987 (2022)
Thampi, A.: Interpretable AI: Building Explainable Machine Learning Systems. Manning Publications Co. (2022)
Wan, M., Zha, D., Liu, N., Zou, N.: In-processing modeling techniques for machine learning fairness: a survey. ACM Trans. Knowl. Discov. Data 17(3), 1–27 (2023)
Woodworth, B., Gunasekar, S., Ohannessian, M.I., Srebro, N.: Learning non-discriminatory predictors. In: Conference on Learning Theory, pp. 1920–1953. PMLR (2017)
Xue, B., Zhang, M., Browne, W.N., Yao, X.: A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evol. Comput. 20(4), 606–626 (2015)
Zhou, Y., Booth, S., Ribeiro, M.T., Shah, J.: Do feature attribution methods correctly attribute features? In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 9623–9633 (2022)
Acknowledgments
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yang, Z., Wang, Z., Huang, C., Yao, X. (2023). An Explainable Feature Selection Approach for Fair Machine Learning. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14261. Springer, Cham. https://doi.org/10.1007/978-3-031-44198-1_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-44198-1_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-44197-4
Online ISBN: 978-3-031-44198-1
eBook Packages: Computer ScienceComputer Science (R0)