The widespread adoption of Machine Learning systems, especially in more decision-critical applications such as criminal sentencing and bank loans, has led to increased concerns about fairness implications. Algorithms and metrics have been developed to mitigate and measure these discriminations. More recently, works have identified a more challenging form of bias called intersectional bias, which encompasses multiple sensitive attributes, such as race and gender, together. In this survey, we review the state-of-the-art in intersectional fairness. We present a taxonomy for intersectional notions of fairness and mitigation. Finally, we identify the key challenges and provide researchers with guidelines for future directions.

翻译：机器学习系统的大规模应用，尤其是在刑事量刑、银行贷款等关键决策领域，引发了人们对公平性影响的日益关注。为缓解和衡量这些歧视，相关算法和度量指标已被开发出来。近期研究识别出一种更具挑战性的偏见形式——交叉偏见，该偏见同时涵盖种族、性别等多个敏感属性。本综述回顾了交叉公平性领域的最新进展，提出了一种针对交叉公平性概念与缓解方法的分类体系，并最终明确了关键挑战，为研究者提供了未来方向的指导方针。