Bias and Discrimination in AI: a cross-disciplinary perspective

Authors: Xavier Ferrer, Tom van Nuenen, Jose M. Such, Mark Coté, Natalia Criado

arXiv: 2008.07309v1 - DOI (cs.CY)
License: ASSUMED 1991-2003

Abstract: With the widespread and pervasive use of Artificial Intelligence (AI) for automated decision-making systems, AI bias is becoming more apparent and problematic. One of its negative consequences is discrimination: the unfair, or unequal treatment of individuals based on certain characteristics. However, the relationship between bias and discrimination is not always clear. In this paper, we survey relevant literature about bias and discrimination in AI from an interdisciplinary perspective that embeds technical, legal, social and ethical dimensions. We show that finding solutions to bias and discrimination in AI requires robust cross-disciplinary collaborations.

Submitted to arXiv on 11 Aug. 2020

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