Hort, Max;
Chen, Zhenpeng;
Zhang, Jie M;
Harman, Mark;
Sarro, Federica;
(2023)
Bias Mitigation for Machine Learning Classifiers: A
Comprehensive Survey.
ACM Journal on Responsible Computing
10.1145/3631326.
(In press).
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Abstract
This paper provides a comprehensive survey of bias mitigation methods for achieving fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning bias mitigation for ML classifiers. These methods can be distinguished based on their intervention procedure (i.e., pre-processing, in-processing, post-processing) and the technique they apply. We investigate how existing bias mitigation methods are evaluated in the literature. In particular, we consider datasets, metrics and benchmarking. Based on the gathered insights (e.g., What is the most popular fairness metric? How many datasets are used for evaluating bias mitigation methods?), we hope to support practitioners in making informed choices when developing and evaluating new bias mitigation methods.
| Type: | Article |
|---|---|
| Title: | Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3631326 |
| Publisher version: | https://doi.org/10.1145/3631326 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10178614 |
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