Construct · 4 names · 5 citations
Different names. Different fields. One construct. One map.
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The construct at its named source: after watching an algorithm make a mistake, people abandon it more readily than they abandon a human forecaster who makes the same mistake, even when the algorithm is the more accurate predictor. Dietvorst, Simmons, and Massey's 2015 Algorithm aversion: People erroneously avoid algorithms after seeing them err showed this across forecasting tasks and gave the pattern its name. This laboratory finding is the same reluctance that the older prediction literature, the advice studies, and the consumer research each name and measure in their own way.[1]
The historical root of the same reluctance: across decades of studies, simple statistical or mechanical rules predicted outcomes such as diagnosis, recidivism, and academic success at least as well as trained experts, yet practitioners kept preferring their own judgment. Dawes, Faust, and Meehl's 1989 Clinical versus actuarial judgment, building on Meehl's 1954 monograph Clinical versus statistical prediction, reviewed this evidence and the reasons clinicians resisted the formula. Here the reluctance wears the older name of a preference for clinical over actuarial judgment, but it is the same construct that Dietvorst and colleagues later relabelled as algorithm aversion.[2][3]
The same construct measured as a continuous weight on advice, where the direction of the finding can flip: in advice-taking studies that label the source, people often shift their own estimates toward identical advice more when told it came from an algorithm than from a person. Logg, Minson, and Moore's 2019 Algorithm appreciation: People prefer algorithmic to human judgment reported this pattern and framed it as the mirror of aversion. On this map's reading, aversion and appreciation are two readings on one scale: how much weight a person gives an algorithm's advice, which task, framing, and error history move up or down.[4]
The same reluctance studied as consumer behaviour: people are less willing to use healthcare delivered by artificial intelligence than identical care from a human, and pay less for it, because they believe a provider that is a machine cannot account for their individual traits, a belief the authors call uniqueness neglect. Longoni, Bonezzi, and Morewedge's 2019 Resistance to Medical Artificial Intelligence documented this effect and its mechanism across many studies. Framed in marketing as consumer resistance to a product, it is once more the reluctance to rely on an algorithm that the decision, prediction, and advice literatures each name in their own terms.[5]
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