Fairness in AI: challenges in bridging the gap between algorithms and law
<|reference_start|>Fairness in AI: challenges in bridging the gap between algorithms and law: 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<|reference_end|>
@article{giannopoulos2024fairness,
    title={Fairness in AI: challenges in bridging the gap between algorithms and
  law},
    author={Giorgos Giannopoulos, Maria Psalla, Loukas Kavouras, Dimitris
  Sacharidis, Jakub Marecek, German M Matilla and Ioannis Emiris},
    journal={arXiv preprint arXiv:2404.19371},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.19371},
    primaryClass={cs.CY}
}