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

TL;DR

This paper explores the intersection of algorithmic fairness and law, highlighting challenges and proposing best practices for implementing fairness in AI systems within legal frameworks in the EU and US.

Contribution

It provides a legal and ethical perspective on fairness, introduces fairness definitions with accessible explanations, and offers criteria and best practices for real-world AI fairness implementation.

Findings

Legal frameworks influence fairness definition choices

Guidelines for selecting appropriate fairness measures

Best practices for deploying fairness in 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