Fairness Testing: A Comprehensive Survey and Analysis of Trends

被引：8
作者：
Chen, Zhenpeng [1 ]
Zhang, Jie M. [2 ]
Hort, Max [3 ]
Harman, Mark
Sarro, Federica
机构：
[1] UCL, Dept Comp Sci, Gower St, London WC1E 6BT, England
[2] Kings Coll London, Dept Informat, 30 Aldwych, London WC2B 4BG, England
[3] Simula Res Lab, Kristian Augusts Gate 23, N-0164 Oslo, Norway
来源：
ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY | 2024年 / 33卷 / 05期
关键词：
Machine learning; fairness testing; survey; analysis; trends; ALGORITHMIC FAIRNESS; BIAS; PREDICTION; IMPACT;
D O I：
10.1145/3652155
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and concern among software engineers. To tackle this issue, extensive research has been dedicated to conducting fairness testing of ML software, and this article offers a comprehensive survey of existing studies in this field. We collect 100 papers and organize them based on the testing workflow (i.e., how to test) and testing components (i.e., what to test). Furthermore, we analyze the research focus, trends, and promising directions in the realm of fairness testing. We also identify widely adopted datasets and open-source tools for fairness testing.
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页数：59
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