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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Preacher, K. J., & Hayes, A. F. (2008) · Behavior Research Methods · doi:10.3758/brm.40.3.879 · Cited by 34,117 · cited 1×
“We then used Preacher and Hayes’s (2008) bootstrapping procedure”
Method
Algorithm aversion: People erroneously avoid algorithms after seeing them err.
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015) · Journal of Experimental Psychology: General · doi:10.1037/xge0000033 · Cited by 3,232 · cited 6×
“Dietvorst, Simmons, and Massey (2015) gave participants the choice of either exclusively using an algorithm’s forecasts or exclusively using their own forecasts during an incentivized forecasting task, and manipulated whether participants had experience with the algorithm prior to making this…”
BackgroundResultInfluentialSelf-citation
“Clinical versus Actuarial Judgment.”
Dawes, R. M., Faust, D., & Meehl, P. E. (1989) · Science · doi:10.1126/science.2648573 · Cited by 2,298
Clinical versus statistical prediction: A theoretical analysis and a review of the evidence.
Meehl, P. E. (1954) · University of Minnesota Press eBooks · doi:10.1037/11281-000 · Cited by 2,227
The robust beauty of improper linear models in decision making.
Dawes, R. M. (1979) · American Psychologist · doi:10.1037/0003-066x.34.7.571 · Cited by 2,046
Clinical versus mechanical prediction: a meta-analysis
Grove, W. M., Zald, D. H., Lebow, B. S., Snitz, B. E., & Nelson, C. (2000) · Psychological Assessment · doi:10.1037/1040-3590.12.1.19 · Cited by 1,640 · cited 2×
“…of recidivism (Thompson, 1952; Wormith & Goldstone, 1984), medical diagnoses (Adams et al., 1986; Beck et al., 2011; Dawes, Faust, & Meehl, 1989; Grove et al., 2000), demand for products (Schweitzer & Cachon, 2000), and so on (for reviews see Dawes et al., 1989; Grove et al., 2000; Meehl, 1954).”
Background
Decision Bias in the Newsvendor Problem with a Known Demand Distribution: Experimental Evidence
Schweitzer, M. E., & Cachon, G. P. (2000) · Management Science · doi:10.1287/mnsc.46.3.404.12070 · Cited by 1,077 · cited 2×
“…of recidivism (Thompson, 1952; Wormith & Goldstone, 1984), medical diagnoses (Adams et al., 1986; Beck et al., 2011; Dawes, Faust, & Meehl, 1989; Grove et al., 2000), demand for products (Schweitzer & Cachon, 2000), and so on (for reviews see Dawes et al., 1989; Grove et al., 2000; Meehl, 1954).”
Background
Advice taking and decision-making: An integrative literature review, and implications for the organizational sciences
Bonaccio, S., & Dalal, R. S. (2006) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2006.07.001 · Cited by 1,011 · cited 1×
“For example, people often give less weight to other people’s advice than they should when left to their own devices (see Bonaccio & Dalal, 2006); however, it is possible that people will commit to using another person’s advice in a constrained manner.”
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Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival
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“…(Dawes, 1971; Dawes, 1979), prisoners’ likelihood of recidivism (Thompson, 1952; Wormith & Goldstone, 1984), medical diagnoses (Adams et al., 1986; Beck et al., 2011; Dawes, Faust, & Meehl, 1989; Grove et al., 2000), demand for products (Schweitzer & Cachon, 2000), and so on (for reviews see…”
Forecasting and Planning: An Evaluation
Hogarth, R. M., & Makridakis, S. (1981) · Management Science · doi:10.1287/mnsc.27.2.115 · Cited by 614 · cited 2×
“…people’s attempts to adjust algorithmic forecasts often make them worse (e.g. Carbone, Andersen, Corriveau, & Corson, 1983; Goodwin & Fildes, 1999; Hogarth & Makridakis, 1981; Lim & O'Connor, 1995; Willemain, 1991), the benefits associated with getting people to use the algorithm may outweigh the…”
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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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