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Likelihood of Questioning AI-Based Recommendations Due to Perceived Racial/Gender Bias

Carlos M. Parra Orcid Logo, Manjul Gupta, Denis Dennehy Orcid Logo

IEEE Transactions on Technology and Society, Volume: 3, Issue: 1, Pages: 41 - 45

Swansea University Author: Denis Dennehy Orcid Logo

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DOI (Published version): 10.1109/tts.2021.3120303

Abstract

Advances in artificial intelligence (AI) are giving rise to a multitude of AI-embedded technologies that are increasingly impacting all aspects of modern society. Yet, there is a paucity of rigorous research that advances understanding of when, and which type of, individuals are more likely to quest...

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Published in: IEEE Transactions on Technology and Society
ISSN: 2637-6415
Published: Institute of Electrical and Electronics Engineers (IEEE) 2022
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa59910
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Abstract: Advances in artificial intelligence (AI) are giving rise to a multitude of AI-embedded technologies that are increasingly impacting all aspects of modern society. Yet, there is a paucity of rigorous research that advances understanding of when, and which type of, individuals are more likely to question AI-based recommendations due to perceived racial and gender bias. This study, which is part of a larger research stream contributes to knowledge by using a scenario-based survey that was issued to a sample of 387 U.S. participants. The findings suggest that considering perceived racial and gender bias, human resource (HR) recruitment and financial product/service procurement scenarios exhibit a higher questioning likelihood. Meanwhile, the healthcare scenario presents the lowest questioning likelihood. Furthermore, in the context of this study, U.S. participants tend to be more susceptible to questioning AI-based recommendations due to perceived racial bias rather than gender bias.
College: Faculty of Humanities and Social Sciences
Issue: 1
Start Page: 41
End Page: 45

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