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Questioning Racial and Gender Bias in AI-based Recommendations: Do Espoused National Cultural Values Matter?

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

Information Systems Frontiers

Swansea University Author: Denis Dennehy Orcid Logo

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DOI (Published version): 10.1007/s10796-021-10156-2

Abstract

One realm of AI, recommender systems have attracted significant research attention due to concerns about its devastating effects to society’s most vulnerable and marginalised communities. Both media press and academic literature provide compelling evidence that AI-based recommendations help to perpe...

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Published in: Information Systems Frontiers
ISSN: 1387-3326 1572-9419
Published: Springer Science and Business Media LLC 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa59527
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first_indexed 2022-03-11T12:03:30Z
last_indexed 2022-03-31T03:26:09Z
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spelling 2022-03-30T16:25:57.1621062 v2 59527 2022-03-08 Questioning Racial and Gender Bias in AI-based Recommendations: Do Espoused National Cultural Values Matter? ba782cbe94139075e5418dc9274e8304 0000-0001-9931-762X Denis Dennehy Denis Dennehy true false 2022-03-08 BBU One realm of AI, recommender systems have attracted significant research attention due to concerns about its devastating effects to society’s most vulnerable and marginalised communities. Both media press and academic literature provide compelling evidence that AI-based recommendations help to perpetuate and exacerbate racial and gender biases. Yet, there is limited knowledge about the extent to which individuals might question AI-based recommendations when perceived as biased. To address this gap in knowledge, we investigate the effects of espoused national cultural values on AI questionability, by examining how individuals might question AI-based recommendations due to perceived racial or gender bias. Data collected from 387 survey respondents in the United States indicate that individuals with espoused national cultural values associated to collectivism, masculinity and uncertainty avoidance are more likely to question biased AI-based recommendations. This study advances understanding of how cultural values affect AI questionability due to perceived bias and it contributes to current academic discourse about the need to hold AI accountable. Journal Article Information Systems Frontiers Springer Science and Business Media LLC 1387-3326 1572-9419 Artificial intelligence; Recommender systems; Culture; Racial bias; Gender bias; Responsible AI; Algorithmic bias; Ethical AI 20 6 2021 2021-06-20 10.1007/s10796-021-10156-2 COLLEGE NANME Business COLLEGE CODE BBU Swansea University The IReL Consortium 2022-03-30T16:25:57.1621062 2022-03-08T14:32:42.8261827 Faculty of Humanities and Social Sciences School of Management - Business Management Manjul Gupta 1 Carlos M. Parra 2 Denis Dennehy 0000-0001-9931-762X 3 59527__23743__624f861ee9994a07a31ba33565121174.pdf 59527.pdf 2022-03-30T16:09:01.1222621 Output 724908 application/pdf Version of Record true Copyright: The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License true eng http://creativecommons.org/licenses/by/4.0/
title Questioning Racial and Gender Bias in AI-based Recommendations: Do Espoused National Cultural Values Matter?
spellingShingle Questioning Racial and Gender Bias in AI-based Recommendations: Do Espoused National Cultural Values Matter?
Denis Dennehy
title_short Questioning Racial and Gender Bias in AI-based Recommendations: Do Espoused National Cultural Values Matter?
title_full Questioning Racial and Gender Bias in AI-based Recommendations: Do Espoused National Cultural Values Matter?
title_fullStr Questioning Racial and Gender Bias in AI-based Recommendations: Do Espoused National Cultural Values Matter?
title_full_unstemmed Questioning Racial and Gender Bias in AI-based Recommendations: Do Espoused National Cultural Values Matter?
title_sort Questioning Racial and Gender Bias in AI-based Recommendations: Do Espoused National Cultural Values Matter?
author_id_str_mv ba782cbe94139075e5418dc9274e8304
author_id_fullname_str_mv ba782cbe94139075e5418dc9274e8304_***_Denis Dennehy
author Denis Dennehy
author2 Manjul Gupta
Carlos M. Parra
Denis Dennehy
format Journal article
container_title Information Systems Frontiers
publishDate 2021
institution Swansea University
issn 1387-3326
1572-9419
doi_str_mv 10.1007/s10796-021-10156-2
publisher Springer Science and Business Media LLC
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 1
active_str 0
description One realm of AI, recommender systems have attracted significant research attention due to concerns about its devastating effects to society’s most vulnerable and marginalised communities. Both media press and academic literature provide compelling evidence that AI-based recommendations help to perpetuate and exacerbate racial and gender biases. Yet, there is limited knowledge about the extent to which individuals might question AI-based recommendations when perceived as biased. To address this gap in knowledge, we investigate the effects of espoused national cultural values on AI questionability, by examining how individuals might question AI-based recommendations due to perceived racial or gender bias. Data collected from 387 survey respondents in the United States indicate that individuals with espoused national cultural values associated to collectivism, masculinity and uncertainty avoidance are more likely to question biased AI-based recommendations. This study advances understanding of how cultural values affect AI questionability due to perceived bias and it contributes to current academic discourse about the need to hold AI accountable.
published_date 2021-06-20T04:16:55Z
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score 11.037319

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