Bias mitigation methods for binary classification decision-making systems have been widely researched due to the ever-growing importance of designing fair machine learning processes that are impartial and do not discriminate against individuals or groups based on protected personal characteristics. In this paper, we present a structured overview of the research landscape for bias mitigation methods, report on their benefits and limitations, and provide recommendations for the development of future bias mitigation methods for binary classification.

翻译：鉴于设计公平无偏、不基于受保护个人特征对个体或群体产生歧视的机器学习流程日益重要，针对二分类决策系统的偏差缓解方法已得到广泛研究。本文对偏差缓解方法的研究现状进行了结构化综述，报告了其优势与局限性，并为未来面向二分类任务的偏差缓解方法开发提供了建议。