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：机器学习系统的广泛采用，尤其是在刑事量刑和银行贷款等更关键的决策应用中，引发了人们对公平性影响的日益关注。为了减轻和衡量这些歧视，人们已经开发了相关算法和指标。近期，研究还发现了一种更具挑战性的偏见形式，称为交叉偏见，它同时包含多个敏感属性，例如种族和性别。本综述回顾了交叉公平性的前沿研究。我们提出了一种用于交叉公平性概念和缓解措施的分类体系。最后，我们指出了关键挑战，并为研究人员提供了未来方向的指导。