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+ Software Fairness
+Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
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+ About
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+ 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.
+Resources
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+ Preprint
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+ Online Appendix
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+ Authors
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+ Max Hort
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+ Zhenpeng Chen
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+ Jie Zhang
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+ Federica Sarro
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+ Mark Harman
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+ Acknowledgement
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+ EPIC
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