No Cover Image

Journal article 700 views 80 downloads

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

  • 59910.pdf

    PDF | Version of Record

    This work is licensed under a Creative Commons Attribution 4.0 License

    Download (766.59KB)

Check full text

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...

Full description

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
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2022-04-27T11:58:10Z
last_indexed 2022-05-05T03:32:10Z
id cronfa59910
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2022-05-04T11:18:06.5961644</datestamp><bib-version>v2</bib-version><id>59910</id><entry>2022-04-27</entry><title>Likelihood of Questioning AI-Based Recommendations Due to Perceived Racial/Gender Bias</title><swanseaauthors><author><sid>ba782cbe94139075e5418dc9274e8304</sid><ORCID>0000-0001-9931-762X</ORCID><firstname>Denis</firstname><surname>Dennehy</surname><name>Denis Dennehy</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2022-04-27</date><deptcode>BBU</deptcode><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.</abstract><type>Journal Article</type><journal>IEEE Transactions on Technology and Society</journal><volume>3</volume><journalNumber>1</journalNumber><paginationStart>41</paginationStart><paginationEnd>45</paginationEnd><publisher>Institute of Electrical and Electronics Engineers (IEEE)</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>2637-6415</issnElectronic><keywords/><publishedDay>16</publishedDay><publishedMonth>3</publishedMonth><publishedYear>2022</publishedYear><publishedDate>2022-03-16</publishedDate><doi>10.1109/tts.2021.3120303</doi><url/><notes/><college>COLLEGE NANME</college><department>Business</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>BBU</DepartmentCode><institution>Swansea University</institution><apcterm>Not Required</apcterm><lastEdited>2022-05-04T11:18:06.5961644</lastEdited><Created>2022-04-27T12:54:40.0454942</Created><path><level id="1">Faculty of Humanities and Social Sciences</level><level id="2">School of Management - Business Management</level></path><authors><author><firstname>Carlos M.</firstname><surname>Parra</surname><orcid>0000-0001-6029-4512</orcid><order>1</order></author><author><firstname>Manjul</firstname><surname>Gupta</surname><order>2</order></author><author><firstname>Denis</firstname><surname>Dennehy</surname><orcid>0000-0001-9931-762X</orcid><order>3</order></author></authors><documents><document><filename>59910__23977__3a96736d03ba44759295ac5785b15598.pdf</filename><originalFilename>59910.pdf</originalFilename><uploaded>2022-05-04T11:04:42.6066754</uploaded><type>Output</type><contentLength>784987</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>This work is licensed under a Creative Commons Attribution 4.0 License</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2022-05-04T11:18:06.5961644 v2 59910 2022-04-27 Likelihood of Questioning AI-Based Recommendations Due to Perceived Racial/Gender Bias ba782cbe94139075e5418dc9274e8304 0000-0001-9931-762X Denis Dennehy Denis Dennehy true false 2022-04-27 BBU 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. Journal Article IEEE Transactions on Technology and Society 3 1 41 45 Institute of Electrical and Electronics Engineers (IEEE) 2637-6415 16 3 2022 2022-03-16 10.1109/tts.2021.3120303 COLLEGE NANME Business COLLEGE CODE BBU Swansea University Not Required 2022-05-04T11:18:06.5961644 2022-04-27T12:54:40.0454942 Faculty of Humanities and Social Sciences School of Management - Business Management Carlos M. Parra 0000-0001-6029-4512 1 Manjul Gupta 2 Denis Dennehy 0000-0001-9931-762X 3 59910__23977__3a96736d03ba44759295ac5785b15598.pdf 59910.pdf 2022-05-04T11:04:42.6066754 Output 784987 application/pdf Version of Record true This work is licensed under a Creative Commons Attribution 4.0 License true eng https://creativecommons.org/licenses/by/4.0/
title Likelihood of Questioning AI-Based Recommendations Due to Perceived Racial/Gender Bias
spellingShingle Likelihood of Questioning AI-Based Recommendations Due to Perceived Racial/Gender Bias
Denis Dennehy
title_short Likelihood of Questioning AI-Based Recommendations Due to Perceived Racial/Gender Bias
title_full Likelihood of Questioning AI-Based Recommendations Due to Perceived Racial/Gender Bias
title_fullStr Likelihood of Questioning AI-Based Recommendations Due to Perceived Racial/Gender Bias
title_full_unstemmed Likelihood of Questioning AI-Based Recommendations Due to Perceived Racial/Gender Bias
title_sort Likelihood of Questioning AI-Based Recommendations Due to Perceived Racial/Gender Bias
author_id_str_mv ba782cbe94139075e5418dc9274e8304
author_id_fullname_str_mv ba782cbe94139075e5418dc9274e8304_***_Denis Dennehy
author Denis Dennehy
author2 Carlos M. Parra
Manjul Gupta
Denis Dennehy
format Journal article
container_title IEEE Transactions on Technology and Society
container_volume 3
container_issue 1
container_start_page 41
publishDate 2022
institution Swansea University
issn 2637-6415
doi_str_mv 10.1109/tts.2021.3120303
publisher Institute of Electrical and Electronics Engineers (IEEE)
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 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.
published_date 2022-03-16T04:17:34Z
_version_ 1763754172298035200
score 11.017797

Similar Items