Fairness in AI: challenges in bridging the gap between algorithms and law

TL;DR

The paper addresses the problem of aligning algorithmic fairness with European and US anti-discrimination law to support safe real-world deployment. It combines a legal survey with a concrete set of algorithmic fairness definitions and a framework of criteria to guide the selection and deployment of fairness methods while respecting legal norms. Key findings identify proxy discrimination, intersectionality, and feedback loops as central challenges with no one-size-fits-all solution, underscoring the need for domain-specific expertise and governance. By advocating cross-sector collaboration and systematic guidelines, the work aims to bridge law, ethics, and data practice, informing policy and governance for fair-by-design AI systems.

Abstract

In this paper we examine algorithmic fairness from the perspective of law aiming to identify best practices and strategies for the specification and adoption of fairness definitions and algorithms in real-world systems and use cases. We start by providing a brief introduction of current anti-discrimination law in the European Union and the United States and discussing the concepts of bias and fairness from an legal and ethical viewpoint. We then proceed by presenting a set of algorithmic fairness definitions by example, aiming to communicate their objectives to non-technical audiences. Then, we introduce a set of core criteria that need to be taken into account when selecting a specific fairness definition for real-world use case applications. Finally, we enumerate a set of key considerations and best practices for the design and employment of fairness methods on real-world AI applications