Algorithmic bias: review, synthesis, and future research directions
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.
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Algorithmic bias can arise from several sources, including biased feature selection, biased model design, or biased decision rules.
“Algorithmic bias can arise from several sources, including biased feature selection, biased model design, or biased decision rules [47,49].”
From Ethical Considerations of Using ChatGPT in Health Care (2023) · cites this paper as [47]
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- Authors
- Kordzadeh, N., & Ghasemaghaei, M.
- Affiliations
- Worcester Polytechnic Institute (US)
- McMaster University (CA)
- Corresponding author
- Kordzadeh, Nima
- Venue
- European Journal of Information Systems
- Publisher
- Informa UK Limited
- Published
- 2021-06-06
- Volume
- 31
- Issue
- 3
- Pages
- 388-409
- ISSN
- 0960-085X (ISSN-L)
- 1476-9344
- OpenAlex
- W3170933632
- Cited by
- 770
- References
- 20
- Citations by year
- 2026: 230 · 2025: 235 · 2024: 178 · 2023: 93 · 2022: 28 · 2021: 4 · 2020: 1
- FWCI
- 94.72
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- European Journal of Information Systems (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Keywords
- algorithmic bias, data-driven decision making, perceived fairness, technology acceptance, algorithm appreciation, system adoption, stimulus-organism-response model, workplace inequality, research model
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