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

被引：176

作者：

Akter, Shahriar ^{[1
]}

McCarthy, Grace ^{[1
]}

Sajib, Shahriar ^{[2
]}

Michael, Katina ^{[3
]}

Dwivedi, Yogesh K. ^{[4
,5
]}

D'Ambra, John ^{[6
]}

Shen, K. N. ^{[7
]}

机构：

[1] Univ Wollongong, Sch Business, Wollongong, NSW 2522, Australia

[2] Univ Technol Sydney, UTS Business Sch, 15 Broadway, Ultimo, NSW 2007, Australia

[3] Arizona State Univ, Sch Future Innovat Soc, Mailcode 5603, Tempe, AZ USA

[4] Swansea Univ, Sch Management, Emerging Markets Res Ctr EMaRC, Bay Campus, Swansea SA1 8EN, W Glam, Wales

[5] Pune & Symbiosis Int Deemed Univ, Symbiosis Inst Business Management, Pune, Maharashtra, India

[6] Univ New South Wales Sydney Australia, UNSW Sydney, Sch Informat Syst & Technol Management, Sydney, NSW 2052, Australia

[7] United Arab Emirates Univ, Coll Business & Econ, Innovat Technol & Entrepreneurship, POB 15551, Al Ain, U Arab Emirates

来源：

INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT | 2021年 / 60卷 / 60期

关键词：

Algorithmic bias; Data driven innovation; Data bias; Method bias; Societal bias; DYNAMIC MANAGERIAL CAPABILITIES; BIG DATA ANALYTICS; ARTIFICIAL-INTELLIGENCE; FUTURE; MICROFOUNDATIONS; MANAGEMENT; REVOLUTION; FAIRNESS; SYSTEMS; SERVICE;

D O I：

10.1016/j.ijinfomgt.2021.102387

中图分类号：

G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];

学科分类号：

050302 [传播学]; 071101 [系统理论];

摘要：

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.

引用

页数：13

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