Bias and Discrimination in AI: a cross-disciplinary perspective

Xavier Ferrer Aran; Tom van Nuenen; Jose Such; Mark Coté; Natalia Criado Pacheco

doi:10.1109/MTS.2021.3056293

Bias and Discrimination in AI: a cross-disciplinary perspective

Xavier Ferrer Aran, Tom van Nuenen, Jose Such, Mark Coté, Natalia Criado Pacheco

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151 Citations (Scopus)

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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.

Original language	English
Pages (from-to)	72
Number of pages	80
Journal	IEEE TECHNOLOGY AND SOCIETY MAGAZINE
Volume	40
Issue number	2
DOIs	https://doi.org/10.1109/MTS.2021.3056293 https://doi.org/10.1109/MTS.2021.3056293
Publication status	Accepted/In press - 2020

Keywords

Fairness
Bias
Discrimination

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10.1109/MTS.2021.3056293Licence: CC BY-ND
10.1109/MTS.2021.3056293Licence: Unspecified

IEEE_survey_short_bias_and_discrimination_in_AI__copy_camera_ready_Tech_Society_ (2)Licence: Unspecified

DADD: Discovering and Attesting Digital Discrimination
Such, J. (Primary Investigator), Tasioulas, J. (Co-Investigator), Nelken, D. (Co-Investigator), Viganò, L. (Co-Investigator), Criado Pacheco, N. (Co-Investigator), Hedges, M. (Co-Investigator) & Coté, M. (Co-Investigator)
EPSRC Engineering and Physical Sciences Research Council
1/08/2018 → 31/05/2022
Project: Research

Cite this

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title = "Bias and Discrimination in AI: a cross-disciplinary perspective",

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.",

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PY - 2020

Y1 - 2020

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KW - Bias

KW - Discrimination

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M3 - Article

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ER -

Bias and Discrimination in AI: a cross-disciplinary perspective

Abstract

Keywords

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Projects

DADD: Discovering and Attesting Digital Discrimination

Cite this