AI-driven decision-making can lead to discrimination against certain individuals or social groups based on protected characteristics/attributes such as race, gender, or age. The domain of fairness-aware machine learning focuses on methods and algorithms for understanding, mitigating, and accounting for bias in AI/ML models. Still, thus far, the vast majority of the proposed methods assess fairness based on a single protected attribute, e.g. only gender or race. In reality, though, human identities are multi-dimensional, and discrimination can occur based on more than one protected characteristic, leading to the so-called ``multi-dimensional discrimination'' or ``multi-dimensional fairness'' problem. While well-elaborated in legal literature, the multi-dimensionality of discrimination is less explored in the machine learning community. Recent approaches in this direction mainly follow the so-called intersectional fairness definition from the legal domain, whereas other notions like additive and sequential discrimination are less studied or not considered thus far. In this work, we overview the different definitions of multi-dimensional discrimination/fairness in the legal domain as well as how they have been transferred/ operationalized (if) in the fairness-aware machine learning domain. By juxtaposing these two domains, we draw the connections, identify the limitations, and point out open research directions.

翻译：基于人工智能的决策可能导致针对特定个人或社会群体因受保护特征/属性（如种族、性别或年龄）而产生的歧视。公平感知机器学习的领域专注于理解、缓解和核算人工智能/机器学习模型中偏见的方法与算法。然而，迄今为止，绝大多数提出的方法仅基于单一受保护属性（例如仅性别或种族）来评估公平性。但在现实中，人类身份是多维的，歧视可能基于多个受保护特征而发生，从而引发所谓的"多维歧视"或"多维公平"问题。尽管在法律文献中已有详尽阐述，但歧视的多维性在机器学习社区中尚较少被探索。近期在此方向上的研究主要遵循法律领域中所谓的交叉性公平定义，而其他概念如加性歧视和序贯歧视则较少被研究或迄今尚未被考虑。在本工作中，我们综述了法律领域中多维歧视/公平的不同定义，以及它们如何在（若有时）公平感知机器学习领域中被迁移/操作化。通过并列这两个领域，我们梳理了关联性，识别了局限性，并指出了开放的研究方向。