A systematic review of algorithm aversion in augmented decision making
Abstract
Despite abundant literature theorizing societal implications of algorithmic decision making, relatively little is known about the conditions that lead to the acceptance or rejection of algorithmically generated insights by individual users of decision aids. More specifically, recent findings of algorithm aversion—the reluctance of human forecasters to use superior but imperfect algorithms—raise questions about whether joint human‐algorithm decision making is feasible in practice. In this paper, we systematically review the topic of algorithm aversion as it appears in 61 peer‐reviewed articles between 1950 and 2018 and follow its conceptual trail across disciplines. We categorize and report on the proposed causes and solutions of algorithm aversion in five themes: expectations and expertise, decision autonomy, incentivization, cognitive compatibility, and divergent rationalities. Although each of the presented themes addresses distinct features of an algorithmic decision aid, human users of the decision aid, and/or the decision making environment, apparent interdependencies are highlighted. We conclude that resolving algorithm aversion requires an updated research program with an emphasis on theory integration. We provide a number of empirical questions that can be immediately carried forth by the behavioral decision making community.
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The extra effort needed to draw on algorithmic decisions comes with heavy investments of time and energy, such as aligning human and algorithmic cognitive systems.
“The extra effort needed to draw on algorithmic deci-sions also comes with heavy investments of time and energy, such as aligning human and algorithmic cogni-tive systems (Burton et al., 2020).”
From Algorithmic management in a work context (2021) · cites this paper as Burton et al., 2020
Burton et al. (2020) attribute the misuse of AI-based systems partly to unaddressed psychological needs of decision makers, like agency, autonomy or control.
“Burton et al. (2020) attribute the misuse of AI-based systems partly to unaddressed psychological needs of decision makers, like agency, autonomy or control.”
From Check the box! How to deal with automation bias in AI-based personnel selection (2023) · cites this paper as Burton et al. (2020)
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- Authors
- Burton, J. W., Stein, M., & Jensen, T. B.
- Affiliations
- University of London (GB)
- Copenhagen Business School (DK)
- Corresponding author
- Burton, Jason W.
- Venue
- Journal of Behavioral Decision Making
- Publisher
- Wiley
- Published
- 2019-10-23
- Volume
- 33
- Issue
- 2
- Pages
- 220-239
- ISSN
- 0894-3257 (ISSN-L)
- 1099-0771
- OpenAlex
- W2981686111
- Cited by
- 814
- References
- 11
- Citations by year
- 2026: 181 · 2025: 184 · 2024: 165 · 2023: 131 · 2022: 74 · 2021: 57 · 2020: 20 · 2019: 2
- FWCI
- 4.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Behavioral Decision Making (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion)
- CBS Research Portal (Copenhagen Business School) (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- algorithm aversion, decision aids, algorithmic decision-making, decision-making autonomy, incentives, cognitive compatibility, behavioral decision making
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