Fairer Machine Learning Through Multi-objective Evolutionary Learning

Qingquan ZHANG, Jialin LIU, Zeqi ZHANG, Junyi WEN, Bifei MAO, Xin YAO*

*Corresponding author for this work

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

7 Citations (Scopus)


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. © 2021, Springer Nature Switzerland AG.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science ((LNTCS,volume 12894))
EditorsIgor FARKAŠ, Paolo MASULLI, Sebastian OTTE, Stefan WERMTER
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Electronic)9783030863807
ISBN (Print)9783030863791
Publication statusPublished - 2021
Externally publishedYes
Event30th International Conference on Artificial Neural Networks - Bratislava, Slovakia
Duration: 14 Sept 202117 Sept 2021

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameTheoretical Computer Science and General Issues
ISSN (Print)2512-2010
ISSN (Electronic)2512-2029


Conference30th International Conference on Artificial Neural Networks
Abbreviated titleICANN 2021

Bibliographical note

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”.


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


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