Algorithmic bias: review, synthesis, and future research directions

By Nima Kordzadeh, Maryam Ghasemaghaei. 2021

444 citations recorded by the source corpus.

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.

Subfields: Information Systems, Safety Research, Sociology and Political Science, Evaluation & Benchmarks, Safety, Robustness & Interpretability

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Citations in the corpus

Ethical and social risks of harm from Language Models (2021, background): “…applications ranging from diagnostic healthcare algorithms (Obermeyer et al., 2019) to social outcome prediction (Narayanan, 2021); for a more general introduction see (Chouldechova and Roth, 2018; Kordzadeh and Ghasemaghaei, 2021; Mehrabi et al., 2021; Noble, 2018; Zou and Schiebinger, 2018).”
The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review (2024, background): “Remarkably, even when explicit sensitive attributes are omitted from the input, a proficient machine-learning algorithm might still act upon these attributes due to underlying correlations in the data (Kordzadeh & Ghasemaghaei, 2022).”