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Algorithmic bias: review, synthesis, and future research directions

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  • Nima Kordzadeh
  • Maryam Ghasemaghaei

Abstract

As firms are moving towards data-driven decision making, they are facing an emerging problem, namely, algorithmic bias. Accordingly, algorithmic systems can yield socially-biased outcomes, thereby compounding inequalities in the workplace and in society. This paper reviews, summarises, and synthesises the current literature related to algorithmic bias and makes recommendations for future information systems research. Our literature analysis shows that most studies have conceptually discussed the ethical, legal, and design implications of algorithmic bias, whereas only a limited number have empirically examined them. Moreover, the mechanisms through which technology-driven biases translate into decisions and behaviours have been largely overlooked. Based on the reviewed papers and drawing on theories such as the stimulus-organism-response theory and organisational justice theory, we identify and explicate eight important theoretical concepts and develop a research model depicting the relations between those concepts. The model proposes that algorithmic bias can affect fairness perceptions and technology-related behaviours such as machine-generated recommendation acceptance, algorithm appreciation, and system adoption. The model also proposes that contextual dimensions (i.e., individual, task, technology, organisational, and environmental) can influence the perceptual and behavioural manifestations of algorithmic bias. These propositions highlight the significant gap in the literature and provide a roadmap for future studies.

Suggested Citation

  • Nima Kordzadeh & Maryam Ghasemaghaei, 2022. "Algorithmic bias: review, synthesis, and future research directions," European Journal of Information Systems, Taylor & Francis Journals, vol. 31(3), pages 388-409, May.
  • Handle: RePEc:taf:tjisxx:v:31:y:2022:i:3:p:388-409
    DOI: 10.1080/0960085X.2021.1927212
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    Cited by:

    1. Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
    2. Le Cheng & Xiuli Liu & Chunlei Si, 2024. "Identifying stance in legislative discourse: a corpus-driven study of data protection laws," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    3. Jella Pfeiffer & Julia Gutschow & Christian Haas & Florian Möslein & Oliver Maspfuhl & Frederik Borgers & Suzana Alpsancar, 2023. "Algorithmic Fairness in AI," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 65(2), pages 209-222, April.
    4. Hanisch, Marvin & Goldsby, Curtis M. & Fabian, Nicolai E. & Oehmichen, Jana, 2023. "Digital governance: A conceptual framework and research agenda," Journal of Business Research, Elsevier, vol. 162(C).
    5. Mallory Avery & Andreas Leibbrandt & Joseph Vecci, 2023. "Does Artificial Intelligence Help or Hurt Gender Diversity? Evidence from Two Field Experiments on Recruitment in Tech," Monash Economics Working Papers 2023-09, Monash University, Department of Economics.
    6. Clement A. Adebamowo & Shawneequa Callier & Simisola Akintola & Oluchi Maduka & Ayodele Jegede & Christopher Arima & Temidayo Ogundiran & Sally N. Adebamowo, 2023. "The promise of data science for health research in Africa," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    7. Lanqing Du & Jinwook Lee, 2023. "Workforce pDEI: Productivity Coupled with DEI," Papers 2311.11231, arXiv.org, revised Dec 2023.
    8. De Bock, Koen W. & Coussement, Kristof & Caigny, Arno De & Słowiński, Roman & Baesens, Bart & Boute, Robert N. & Choi, Tsan-Ming & Delen, Dursun & Kraus, Mathias & Lessmann, Stefan & Maldonado, Sebast, 2024. "Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda," European Journal of Operational Research, Elsevier, vol. 317(2), pages 249-272.

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