Gender bias in AI-based decision-making systems: a systematic literature review

Nadeem, A; Marjanovic, O; Abedin, B

Gender bias in AI-based decision-making systems: a systematic literature review

Nadeem, A Marjanovic, O

Abedin, B

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Publisher:: Australian Journal of Information Systems
Publication Type:: Journal Article
Citation:: Australasian Journal of Information Systems, 2022, 26
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Nadeem, A
dc.contributor.author	Marjanovic, O https://orcid.org/0000-0001-8362-0982
dc.contributor.author	Abedin, B https://orcid.org/0000-0002-3426-443X
dc.date.accessioned	2023-03-03T04:17:18Z
dc.date.available	2023-03-03T04:17:18Z
dc.date.issued	2022-01-01
dc.identifier.citation	Australasian Journal of Information Systems, 2022, 26
dc.identifier.issn	1449-8618
dc.identifier.issn	1449-8618
dc.identifier.uri	http://hdl.handle.net/10453/166648
dc.description.abstract	The related literature and industry press suggest that artificial intelligence (AI)-based decision-making systems may be biased towards gender, which in turn impacts individuals and societies. The information system (IS) field has recognised the rich contribution of AI-based outcomes and their effects; however, there is a lack of IS research on the management of gender bias in AI-based decision-making systems and its adverse effects. Hence, the rising concern about gender bias in AI-based decision-making systems is gaining attention. In particular, there is a need for a better understanding of contributing factors and effective approaches to mitigating gender bias in AI-based decision-making systems. Therefore, this study contributes to the existing literature by conducting a Systematic Literature Review (SLR) of the extant literature and presenting a theoretical framework for the management of gender bias in AI-based decision-making systems. The SLR results indicate that the research on gender bias in AI-based decision-making systems is not yet well established, highlighting the great potential for future IS research in this area, as articulated in the paper. Based on this review, we conceptualise gender bias in AI-based decision-making systems as a socio-technical problem and propose a theoretical framework that offers a combination of technological, organisational, and societal approaches as well as four propositions to possibly mitigate the biased effects. Lastly, this paper considers future research on the management of gender bias in AI-based decision-making systems in the organisational context.
dc.language	en
dc.publisher	Australian Journal of Information Systems
dc.relation.ispartof	Australasian Journal of Information Systems
dc.relation.isbasedon	10.3127/AJIS.V26I0.3835
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0806 Information Systems, 1503 Business and Management
dc.title	Gender bias in AI-based decision-making systems: a systematic literature review
dc.type	Journal Article
utslib.citation.volume	26
utslib.for	0806 Information Systems
utslib.for	1503 Business and Management
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - HCTD - Human Centred Technology Design
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Professional Practice and Leadership
pubs.organisational-group	/University of Technology Sydney/Strength - PERSWADE - Centre on Persuasive Systems for Wise Adaptive Living
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-03T04:17:16Z
pubs.publication-status	Published
pubs.volume	26

Abstract:

The related literature and industry press suggest that artificial intelligence (AI)-based decision-making systems may be biased towards gender, which in turn impacts individuals and societies. The information system (IS) field has recognised the rich contribution of AI-based outcomes and their effects; however, there is a lack of IS research on the management of gender bias in AI-based decision-making systems and its adverse effects. Hence, the rising concern about gender bias in AI-based decision-making systems is gaining attention. In particular, there is a need for a better understanding of contributing factors and effective approaches to mitigating gender bias in AI-based decision-making systems. Therefore, this study contributes to the existing literature by conducting a Systematic Literature Review (SLR) of the extant literature and presenting a theoretical framework for the management of gender bias in AI-based decision-making systems. The SLR results indicate that the research on gender bias in AI-based decision-making systems is not yet well established, highlighting the great potential for future IS research in this area, as articulated in the paper. Based on this review, we conceptualise gender bias in AI-based decision-making systems as a socio-technical problem and propose a theoretical framework that offers a combination of technological, organisational, and societal approaches as well as four propositions to possibly mitigate the biased effects. Lastly, this paper considers future research on the management of gender bias in AI-based decision-making systems in the organisational context.

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http://hdl.handle.net/10453/166648