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Fairer Machine Learning Through Multi-objective Evolutionary Learning

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 12894)

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.

Keywords

  • Fairness in machine learning
  • Discrimination in machine learning
  • AI ethics
  • Fairness measures
  • Multi-objective learning

This work was supported by the Research Institute of Trustworthy Autonomous Systems, the Guangdong Provincial Key Laboratory (Grant No. 2020B121201001), the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (Grant No. 2017ZT07X386), the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2021A1515011830), the Shenzhen Science and Technology Program (Grant No. KQTD2016112514355531), the Shenzhen Fundamental Research Program (Grant Nos. JCYJ20180504165652917, JCYJ20190809121403553) and Huawei project on “Fundamental Theory and Key Technologies of Trustworthy Systems”.

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Zhang, Q., Liu, J., Zhang, Z., Wen, J., Mao, B., Yao, X. (2021). Fairer Machine Learning Through Multi-objective Evolutionary Learning. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12894. Springer, Cham. https://doi.org/10.1007/978-3-030-86380-7_10

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  • DOI: https://doi.org/10.1007/978-3-030-86380-7_10

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