@inproceedings{7b00a4d96fdb4dbf8f84d321f8c97d64,
title = "Fairer Machine Learning Through Multi-objective Evolutionary Learning",
abstract = "Dilemma between model accuracy and fairness in machine learning models has been shown theoretically and empirically. So far, dozens of fairness measures have been proposed, among which incompatibility and complementarity exist. However, no fairness measure has been universally accepted as the single fairest measure. No one has considered multiple fairness measures simultaneously. In this paper, we propose a multi-objective evolutionary learning framework for mitigating unfairness caused by considering a single measure only, in which a multi-objective evolutionary algorithm is used during training to balance accuracy and multiple fairness measures simultaneously. In our case study, besides the model accuracy, two fairness measures that are conflicting to each other are selected. Empirical results show that our proposed multi-objective evolutionary learning framework is able to find Pareto-front models efficiently and provide fairer machine learning models that consider multiple fairness measures. {\textcopyright} 2021, Springer Nature Switzerland AG.",
keywords = "AI ethics, Discrimination in machine learning, Fairness in machine learning, Fairness measures, Multi-objective learning",
author = "Qingquan ZHANG and Jialin LIU and Zeqi ZHANG and Junyi WEN and Bifei MAO and Xin YAO",
year = "2021",
doi = "10.1007/978-3-030-86380-7_10",
language = "English",
isbn = "9783030863791",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "111--123",
editor = "Igor FARKA{\v S} and MASULLI, {Paolo } and OTTE, {Sebastian } and Stefan WERMTER",
booktitle = "Lecture Notes in Computer Science ((LNTCS,volume 12894))",
address = "Germany",
note = "30th International Conference on Artificial Neural Networks, ICANN 2021 ; Conference date: 14-09-2021 Through 17-09-2021",
}