What constitutes a fair decision? This question is not only difficult for humans but becomes more challenging when Artificial Intelligence (AI) models are used. In light of discriminatory algorithmic behaviors, the EU has recently passed the AI Act, which mandates specific rules for high-risk systems, incorporating both traditional legal non-discrimination regulations and machine learning based algorithmic fairness concepts. This paper aims to bridge these two different concepts in the AI Act through: First, a necessary high-level introduction of both concepts targeting legal and computer science-oriented scholars, and second, an in-depth analysis of the AI Act's relationship between legal non-discrimination regulations and algorithmic fairness. Our analysis reveals three key findings: (1.) Most non-discrimination regulations target only high-risk AI systems. (2.) The regulation of high-risk systems encompasses both data input requirements and output monitoring, though these regulations are partly inconsistent and raise questions of computational feasibility. (3.) Finally, we consider the possible (future) interaction of classical EU non-discrimination law and the AI Act regulations. We recommend developing more specific auditing and testing methodologies for AI systems. This paper aims to serve as a foundation for future interdisciplinary collaboration between legal scholars and computer science-oriented machine learning researchers studying discrimination in AI systems.

翻译：何为公平决策？这一问题不仅对人类而言难以回答，当人工智能（AI）模型被使用时，其挑战性更为凸显。鉴于算法歧视行为的存在，欧盟近期通过了《人工智能法案》，该法案针对高风险系统制定了具体规则，同时纳入了传统法律非歧视条款与基于机器学习的算法公平性概念。本文旨在通过以下两个层面弥合《人工智能法案》中这两种不同概念之间的鸿沟：首先，针对法学与计算机科学领域的学者，对两种概念进行必要的高层次介绍；其次，深入分析《人工智能法案》中法律非歧视条款与算法公平性之间的关联。我们的分析揭示了三个关键发现：（1）大多数非歧视条款仅针对高风险AI系统。（2）对高风险系统的监管同时涵盖数据输入要求与输出监控，尽管这些条款存在部分不一致性，并引发了计算可行性的疑问。（3）最后，我们探讨了传统欧盟非歧视法律与《人工智能法案》条款之间可能（未来）产生的互动。我们建议为AI系统开发更具体的审计与测试方法。本文旨在为研究AI系统歧视问题的法学学者与计算机科学导向的机器学习研究者之间的跨学科合作奠定基础。