  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.
