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

The DOI matches its Crossref record, with a small difference in title or year; 27 of the 27 references it cites were found in a registry or an index.

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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Metadata

Authors
Dietvorst, B. J., Simmons, J. P., & Massey, C.
Affiliations
  • University of Chicago (US)
  • University of Pennsylvania (US)
ORCID
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
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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