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

• URL: http://arxiv.org/abs/2404.19371v1
• Date: Tue, 30 Apr 2024 08:59:00 GMT
• Title: Fairness in AI: challenges in bridging the gap between algorithms and law
• Authors: Giorgos Giannopoulos, Maria Psalla, Loukas Kavouras, Dimitris Sacharidis, Jakub Marecek, German M Matilla, Ioannis Emiris,
• Abstract summary: We identify best practices and strategies for the specification and adoption of fairness definitions and algorithms in real-world systems and use cases. 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.
• Score: 2.651076518493962
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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

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