Fairer Machine Learning Through Multi-objective Evolutionary Learning

Zhang，Qingquan1,2; Liu，Jialin1,2; Zhang，Zeqi3; Wen，Junyi3; Mao，Bifei3; Yao，Xin1,2

10.1007/978-3-030-86380-7_10

2021

0302-9743

1611-3349

LECT NOTES ARTIF INT

12894

111-123

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.

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

第一 ; 通讯

英语

CPCI-S ; EI

Computer Science

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

WOS:000711927100010

20213910947905

Evolutionary algorithms

2-s2.0-85115673628

Scopus

1.Research Institute of Trustworthy Autonomous System,Southern University of Science and Technology (SUSTech),Shenzhen,China
2.Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation,Department of Computer Science and Engineering,Southern University of Science and Technology (SUSTech),Shenzhen,China
3.Trustworthiness Theory Research Center,Huawei Technologies Co.,Ltd.,Shenzhen,China

Zhang，Qingquan,Liu，Jialin,Zhang，Zeqi,et al. Fairer Machine Learning Through Multi-objective Evolutionary Learning[C],2021:111-123.