Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
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.