Algorithmic bias in data-driven innovation in the age of AI

Akter, S; McCarthy, G; Sajib, S; Michael, K; Dwivedi, YK; D'Ambra, J; Shen, KN

Algorithmic bias in data-driven innovation in the age of AI

Akter, S McCarthy, G Sajib, S

Michael, K Dwivedi, YK D'Ambra, J Shen, KN

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: International Journal of Information Management, 2021, 60
Issue Date:: 2021-10-01

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Field	Value	Language
dc.contributor.author	Akter, S
dc.contributor.author	McCarthy, G
dc.contributor.author	Sajib, S https://orcid.org/0000-0003-0209-7718
dc.contributor.author	Michael, K
dc.contributor.author	Dwivedi, YK
dc.contributor.author	D'Ambra, J
dc.contributor.author	Shen, KN
dc.date.accessioned	2022-01-23T19:16:20Z
dc.date.available	2022-01-23T19:16:20Z
dc.date.issued	2021-10-01
dc.identifier.citation	International Journal of Information Management, 2021, 60
dc.identifier.issn	0268-4012
dc.identifier.issn	1873-4707
dc.identifier.uri	http://hdl.handle.net/10453/153451
dc.description.abstract	Data-driven innovation (DDI) gains its prominence due to its potential to transform innovation in the age of AI. Digital giants Amazon, Alibaba, Google, Apple, and Facebook, enjoy sustainable competitive advantages from DDI. However, little is known about algorithmic biases that may present in the DDI process, and result in unjust, unfair, or prejudicial data product developments. Thus, this guest editorial aims to explore the sources of algorithmic biases across the DDI process using a systematic literature review, thematic analysis and a case study on the Robo-Debt scheme in Australia. The findings show that there are three major sources of algorithmic bias: data bias, method bias and societal bias. Theoretically, the findings of our study illuminate the role of the dynamic managerial capability to address various biases. Practically, we provide guidelines on addressing algorithmic biases focusing on data, method and managerial capabilities.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	International Journal of Information Management
dc.relation.isbasedon	10.1016/j.ijinfomgt.2021.102387
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 0806 Information Systems, 0807 Library and Information Studies
dc.subject.classification	Information Systems
dc.title	Algorithmic bias in data-driven innovation in the age of AI
dc.type	Journal Article
utslib.citation.volume	60
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
utslib.for	0807 Library and Information Studies
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
utslib.copyright.status	closed_access	*
dc.date.updated	2022-01-23T19:16:19Z
pubs.publication-status	Published
pubs.volume	60

Abstract:

Data-driven innovation (DDI) gains its prominence due to its potential to transform innovation in the age of AI. Digital giants Amazon, Alibaba, Google, Apple, and Facebook, enjoy sustainable competitive advantages from DDI. However, little is known about algorithmic biases that may present in the DDI process, and result in unjust, unfair, or prejudicial data product developments. Thus, this guest editorial aims to explore the sources of algorithmic biases across the DDI process using a systematic literature review, thematic analysis and a case study on the Robo-Debt scheme in Australia. The findings show that there are three major sources of algorithmic bias: data bias, method bias and societal bias. Theoretically, the findings of our study illuminate the role of the dynamic managerial capability to address various biases. Practically, we provide guidelines on addressing algorithmic biases focusing on data, method and managerial capabilities.

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http://hdl.handle.net/10453/153451