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

Shahriar Akter, Grace McCarthy, Shahriar Sajib, Katina Michael, Yogesh K. Dwivedi, John D'Ambra, K. N. Shen

Research output: Contribution to journalEditorialpeer-review

9 Scopus citations

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.

Original languageEnglish (US)
Article number102387
JournalInternational Journal of Information Management
Volume60
DOIs
StatePublished - Oct 2021

Keywords

  • Algorithmic bias
  • Data bias
  • Data driven innovation
  • Method bias
  • Societal bias

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Computer Networks and Communications
  • Marketing
  • Information Systems and Management
  • Library and Information Sciences
  • Artificial Intelligence

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