Concerns regarding unfairness and discrimination in the context of artificial intelligence (AI) systems have recently received increased attention from both legal and computer science scholars. Yet, the degree of overlap between notions of algorithmic bias and fairness on the one hand, and legal notions of discrimination and equality on the other, is often unclear, leading to misunderstandings between computer science and law. What types of bias and unfairness does the law address when it prohibits discrimination? What role can fairness metrics play in establishing legal compliance? In this paper, we aim to illustrate to what extent European Union (EU) non-discrimination law coincides with notions of algorithmic fairness proposed in computer science literature and where they differ. The contributions of this paper are as follows. First, we analyse seminal examples of algorithmic unfairness through the lens of EU non-discrimination law, drawing parallels with EU case law. Second, we set out the normative underpinnings of fairness metrics and technical interventions and compare these to the legal reasoning of the Court of Justice of the EU. Specifically, we show how normative assumptions often remain implicit in both disciplinary approaches and explain the ensuing limitations of current AI practice and non-discrimination law. We conclude with implications for AI practitioners and regulators.

翻译：近年来，人工智能系统中的不公平性与歧视问题引发了法学与计算机科学学者的广泛关注。然而，算法偏见与公平性的技术概念同法律框架中的歧视与平等概念之间存在显著认知偏差，导致跨学科理解障碍。当法律禁止歧视时，其所针对的偏见与不公究竟涵盖哪些类型？公平性指标在确立法律合规性中能发挥何种作用？本文旨在阐明欧盟非歧视法与计算机科学领域提出的算法公平性概念在多大程度上具有一致性，并揭示两者的本质差异。本文贡献如下：首先，通过欧盟非歧视法视角分析算法非公平性的典型案例，并与欧盟判例法进行类比研究；其次，梳理公平性指标与技术干预措施的规范性基础，将其与欧盟法院的法律推理范式进行对比，着重揭示两种学科路径中隐含的规范性预设，阐明当前AI实践与非歧视法的局限性。最后，提出对AI从业者与监管机构的实践启示。