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

2016/11/04 by Berkeley J. Dietvorst, Joseph P. Simmons, Cade Massey · 1,159 citations
Computer Science · Decision Sciences · #Algorithm #Artificial intelligence #Computer science #Control (management) #Decision-Making and Behavioral Economics #Economics #Explainable Artificial Intelligence (XAI) #Forecasting Techniques and Applications #Imperfect #Machine learning #Microeconomics #Outcome (game theory) #Preference

paper · open access · doi:10.1287/mnsc.2016.2643

published in Management Science 64(3), 1155-1170 (Institute for Operations Research and the Management Sciences)

openalex publication_date 2016/11/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06

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 control—even a slight amount—over an imperfect algorithm’s forecast. Data, as supplemental material, are available at 10.1287/mnsc.2016.2643 . This paper was accepted by Yuval Rottenstreich, judgment and decision making.

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