Cited by 1,230Decision-Making and Behavioral EconomicsForecasting Techniques and ApplicationsHuman-Automation Interaction and Safety

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Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them

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Dietvorst, Berkeley J., Simmons, Joseph P., Massey, Cade·2018·Management Science

DOI 10.1287/mnsc.2016.2643

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Abstract

Although evidence-based algorithms consistently outperform human forecasters, people often fail to use them after learning that they are imperfect, a phenomenon known as algorithm aversion. In this paper, we present three studies investigating how to reduce algorithm aversion. In incentivized forecasting tasks, participants chose between using their own forecasts or those of an algorithm that was built by experts. Participants were considerably more likely to choose to use an imperfect algorithm when they could modify its forecasts, and they performed better as a result. Notably, the preference for modifiable algorithms held even when participants were severely restricted in the modifications they could make (Studies 1–3). In fact, our results suggest that participants’ preference for modifiable algorithms was indicative of a desire for some control over the forecasting outcome, and not for a desire for greater control over the forecasting outcome, as participants’ preference for modifiable algorithms was relatively insensitive to the magnitude of the modifications they were able to make (Study 2). Additionally, we found that giving participants the freedom to modify an imperfect algorithm made them feel more satisfied with the forecasting process, more likely to believe that the algorithm was superior, and more likely to choose to use an algorithm to make subsequent forecasts (Study 3). This research suggests that one can reduce algorithm aversion by giving people some contr…

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Authors: Dietvorst, B. J., Simmons, J. P., & Massey, C.
Affiliations: University of Chicago (US)
University of Pennsylvania (US)
ORCID: Dietvorst, Berkeley J.
Simmons, Joseph P.
Venue: Management Science
Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
Published: 2018-03
Volume: 64
Issue: 3
Pages: 1155-1170
ISSN: 0025-1909 (ISSN-L)
1526-5501
DOI: 10.1287/mnsc.2016.2643
OpenAlex: W3121705224
Semantic Scholar: e1706933e9e1d15515c3a444454c15890e6d134f
Cited by: 1,230
References: 27
Citations by year: 2026: 260 · 2025: 218 · 2024: 203 · 2023: 179 · 2022: 138 · 2021: 104 · 2020: 58 · 2019: 46 · 2018: 16
FWCI: 20.17
Type: journal article
Language: en
Open access: green
doi.org
Locations: Management Science (publishedVersion)
SSRN Electronic Journal (acceptedVersion)
ScholarlyCommons (University of Pennsylvania) (submittedVersion)
Topics: Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
Keywords: algorithm aversion, forecasting, trust in algorithms

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Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them

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