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.

翻译：本文全面综述了在机器学习模型中实现公平性的偏差缓解方法。我们共收集了341篇关于机器学习分类器偏差缓解的文献。这些方法可依据其干预流程（即预处理、处理中、后处理）及所采用技术进行区分。我们研究了现有偏差缓解方法在文献中的评估方式，具体涵盖了数据集、评估指标和基准测试。基于所获见解（例如：最常用的公平性指标是什么？有多少数据集被用于评估偏差缓解方法？），我们期望能帮助实践者在开发与评估新型偏差缓解方法时做出明智选择。