论文标题
缓解机器学习分类器的偏置：一项综合调查
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
论文作者
论文摘要
本文提供了有关在机器学习（ML）模型中实现公平性的偏置缓解方法的全面调查。我们总共收集了341个有关ML分类器偏置缓解的出版物。这些方法可以根据其干预程序（即预处理，进行内部处理，后处理）及其应用的技术来区分。我们研究了文献中如何评估现有的缓解方法。特别是，我们考虑数据集，指标和基准测试。基于收集的见解（例如，最受欢迎的公平度量是什么？用于评估偏见缓解方法的数据集？），我们希望支持从业人员在开发和评估新的偏见缓解方法时做出明智的选择。
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.
