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
Open accessAbstract
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…
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“At the same time, some design choices can reduce resistance -for example, allowing users to adjust the algorithm (Dietvorst et al., 2018)”
From Algorithm aversion in academic libraries: User resistance to AI-enabled discovery and search services (2026) · cites this paper as Dietvorst et al., 2018
In the citing paper
“A lab study by Dietvorst, Simmons, and Massey (2016) showed that being empowered to change or intervene in an algorithmic decision can overcome the aversion reaction sometimes developed by algorithm' users.”
From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Dietvorst, Simmons, and Massey, 2016
In the citing paper
“Their results further indicate that even a very small amount of power of intervention or modification can significantly modify one's reaction and performance when working with the algorithm.”
From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Dietvorst, Simmons, and Massey, 2016
Citations
Cites
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”
MethodAlgorithm 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).”
BackgroundDecision 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).”
BackgroundBonaccio, 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.”
BackgroundSystematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival
Beck, A. H., Sangoi, A. R., Leung, S. C. Y., Marinelli, R. J., Nielsen, T. O., van de Vijver, M. J., West, R. B., van de Rijn, M., & Koller, D. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3002564 · Cited by 780 · cited 1×
“…(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…”
BackgroundA case study of graduate admissions: Application of three principles of human decision making.
Dawes, R. M. (1971) · American Psychologist · doi:10.1037/h0030868 · Cited by 569 · cited 1×
“…of research, has been observed in many different domains, including forecasts of employee performance (see Highhouse, 2008), academic performance (Dawes, 1971; Dawes, 1979), prisoners’ likelihood of recidivism (Thompson, 1952; Wormith & Goldstone, 1984), medical diagnoses (Adams et al., 1986;…”
BackgroundJudgmental forecasting: A review of progress over the last 25years
Lawrence, M. J., Goodwin, P., O’Connor, M. J., & Önkal, D. (2006) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2006.03.007 · Cited by 534
Stubborn Reliance on Intuition and Subjectivity in Employee Selection
Highhouse, S. (2008) · Industrial and Organizational Psychology · doi:10.1111/j.1754-9434.2008.00058.x · Cited by 498 · cited 2×
“…regularity, supported by decades of research, has been observed in many different domains, including forecasts of employee performance (see Highhouse, 2008), academic performance (Dawes, 1971; Dawes, 1979), prisoners’ likelihood of recidivism (Thompson, 1952; Wormith & Goldstone, 1984),…”
BackgroundFildes, R. A., Goodwin, P., Lawrence, M. J., & Nikolopoulos, K. I. (2009) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2008.11.010 · Cited by 447
Computer aided diagnosis of acute abdominal pain: a multicentre study.
Adams, I. D., Chan, M. Q., Clifford, P. C., Cooke, W. M., Dallos, V., de Dombal, F. T., Edwards, M., Hancock, D. M., Hewett, D. J., & McIntyre, N. (1986) · BMJ · doi:10.1136/bmj.293.6550.800 · Cited by 311 · cited 5×
“1 This empirical regularity, supported by decades of research, has been observed in many different domains, including forecasts of employee performance (see Highhouse 2008), academic performance (Dawes 1971, 1979), prisoners’ likelihood of recidivism (Thompson 1952, Wormith and Goldstone 1984), medical diagnoses (Adam…”
BackgroundInfluentialFildes, R. A., & Goodwin, P. (2007) · INFORMS Journal on Applied Analytics · doi:10.1287/inte.1070.0309 · Cited by 225
Factors influencing the use of a decision rule in a probabilistic task
Arkes, H. R., Dawes, R. M., & Christensen, C. (1986) · Organizational Behavior and Human Decision Processes · doi:10.1016/0749-5978(86)90046-4 · Cited by 216
Accepting Error to Make Less Error
Einhorn, H. J. (1986) · Journal of Personality Assessment · doi:10.1207/s15327752jpa5003_8 · Cited by 179 · cited 1×
“Einhorn (1986) proposed that forecasters’ intolerance of algorithms arises because although people believe that algorithms will necessarily err, they believe that humans are capable of perfection (also see Highhouse, 2008).”
BackgroundJudgemental adjustment of initial forecasts: Its effectiveness and biases
Lim, J. S., & O’Connor, M. J. (1995) · Journal of Behavioral Decision Making · doi:10.1002/bdm.3960080302 · Cited by 172
The efficacy of using judgmental versus quantitative forecasting methods in practice
Sanders, N. R., & Manrodt, K. B. (2003) · Omega · doi:10.1016/j.omega.2003.08.007 · Cited by 148 · cited 1×
“Sanders and Manrodt (2003) surveyed 240 firms and found that many did not use algorithms for forecasting, and that firms that did use algorithms made fewer forecasting errors.”
BackgroundThe Clinical and Statistical Prediction of Recidivism
Wormith, J. S., & GOLDSTONE, C. S. (1984) · Criminal Justice and Behavior · doi:10.1177/0093854884011001001 · Cited by 84 · cited 1×
“…of employee performance (see Highhouse, 2008), academic performance (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…”
Carbone, R., Andersen, A., Corriveau, Y., et al. (1983) · Management Science · doi:10.1287/mnsc.29.5.559 · Cited by 77 · cited 1×
“3 Although 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…”
BackgroundSurvey on the use of clinical and mechanical prediction methods in clinical psychology.
Vrieze, S. I., & Grove, W. M. (2009) · Professional Psychology Research and Practice · doi:10.1037/a0014693 · Cited by 60 · cited 1×
“Vrieze and Grove (2009) surveyed 183 clinical psychologists and found that only 31% of them used algorithms when making clinical predictions.”
BackgroundThe effect of graphical adjustment on forecast accuracy
Willemain, T. R. (1991) · International Journal of Forecasting · doi:10.1016/0169-2070(91)90049-2 · Cited by 33 · cited 1×
“…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 costs associated with degrading the algorithm’s…”
BackgroundA Validation of the Glueck Social Prediction Scale for Proneness to Delinquency
Thompson, R. E. (1952) · The Journal of Criminal Law Criminology and Police Science · doi:10.2307/1139334 · Cited by 17 · cited 1×
“…forecasts of employee performance (see Highhouse, 2008), academic performance (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.,…”
BackgroundA violation of dominance and the consumption value of gambling
JOHNSON, J. E. V., O’Brien, R., & Shin, H. S. (1999) · Journal of Behavioral Decision Making · doi:10.1002/(sici)1099-0771(199903)12:1 · Cited by 12
Flagged first, then by citations
Cited by
Sorted by citations
Human trust in artificial intelligence: Review of empirical research
Glikson, E., & Woolley, A. W. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0057 · Cited by 2,921
“For instance, Dzindolet et al. (2003) tested an automated decision aid and found that errors significantly decreased trust and reliance on the aid.”
BackgroundBrave new world: service robots in the frontline
Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018) · Journal of Service Management · doi:10.1108/josm-04-2018-0119 · Cited by 2,315
Algorithm appreciation: People prefer algorithmic to human judgment
Logg, J. M., Minson, J. A., & Moore, D. A. (2019) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2018.12.005 · Cited by 2,061
Resistance to Medical Artificial Intelligence
Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019) · Journal of Consumer Research · doi:10.1093/jcr/ucz013 · Cited by 2,015
Task-Dependent Algorithm Aversion
Castelo, N., Bos, M. W., & Lehmann, D. R. (2019) · Springer eBooks · doi:10.1007/978-3-031-88067-4_21 · Cited by 1,723
Metadata
- Authors
- Dietvorst, B. J., Simmons, J. P., & Massey, C.
- Affiliations
- University of Chicago (US)
- University of Pennsylvania (US)
- Venue
- Management Science
- Published
- 2018-03
- Volume
- 64
- Issue
- 3
- Pages
- 1155-1170
- ISSN
- 0025-1909 (ISSN-L)
- 1526-5501
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
