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Su et al. Intell Robot 2022;2(3):244­74 Intelligence & Robotics
DOI: 10.20517/ir.2022.17

Review Open Access

A review of causality-based fairness machine learning

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Cong Su , Guoxian Yu 1,2 , Jun Wang , Zhongmin Yan , Lizhen Cui 1,2
1 School of Software, Shandong University, Jinan 250101, Shandong, China.
2 SDU-NTU Joint Centre for Artificial Intelligence Research, Shandong University, Jinan 250101, Shandong, China.
Correspondenceto: Prof. GuoxianYu, School of Software, Shandong University, Jinan 250101, Shandong, China. E-mail: gxyu@sdu.edu.cn
How to cite this article: Su C, Yu G, Wang J, Yan Z, Cui L. A review of causality-based fairness machine learning. Intell Robot
2022;2(3):244-74. http://dx.doi.org/10.20517/ir.2022.17
Received: 8 Jun 2022 First Decision: 6 Jul 2022 Revised: 19 Jul 2022 Accepted: 25 Jul 2022 Published: 21 Aug 2022

Academic Editor: Simon X. Yang Copy Editor: Jia-Xin Zhang Production Editor: Jia-Xin Zhang

Abstract
With the wide application of machine learning driven automated decisions (e.g., education, loan approval, and hiring)
in daily life, it is critical to address the problem of discriminatory behavior toward certain individuals or groups. Early
studies focused on defining the correlation/association-based notions, such as statistical parity, equalized odds, etc.
However, recent studies reflect that it is necessary to use causality to address the problem of fairness. This review pro-
vides an exhaustive overview of notions and methods for detecting and eliminating algorithmic discrimination from
a causality perspective. The review begins by introducing the common causality-based definitions and measures
for fairness. We then review causality-based fairness-enhancing methods from the perspective of pre-processing,
in-processing and post-processing mechanisms, and conduct a comprehensive analysis of the advantages, disadvan-
tages, and applicability of these mechanisms. In addition, this review also examines other domains where researchers
have observed unfair outcomes and the ways they have tried to address them. There are still many challenges that hin-
der the practical application of causality-based fairness notions, specifically the difficulty of acquiring causal graphs
and identifiability of causal effects. One of the main purposes of this review is to spark more researchers to tackle
these challenges in the near future.

Keywords: Fairness, causality, fairness-enhancing mechanisms, machine learning, fairness notions

1. INTRODUCTION
Artificial intelligence (AI) techniques are widely applied in various fields to assist people in decision-making,
[5]
[6]
such as hiring [1,2] , loans [3,4] , education , criminal risk assessment , etc. The motivation for using machine

© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0
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