Understanding algorithm aversion: When is advice from automation discounted?

By Andrew Prahl, L. M. Swol. 2017

260 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI (2020, background): “64 Other experiments have produced con-ﬂicting results, suggesting that only expert forecasters are susceptible to algorithm aversion while lay users are more likely to trust algorithmic advice.”
Organizational Behavior and Human Decision Processes (2019, unknown): “And Prahl and Van Swol (2017) cite the paper as evidence that “humans are trusted more than computers” (p. 693).”
A systematic review of algorithm aversion in augmented decision making (2019, methodology): “However, conflicting results exist in which competitive cash rewards did not lead to algorithm aversion (e.g., Prahl & Van Swol, 2017), as well as experiments where algorithm aversion persists in the absence of competitive economic incentivization (e.g., Önkal et al., 2009).”
Algorithmic management in a work context (2021, background): “Algorithm aversion refers to people’s hesitance to use algorithmic results, usually after observing imperfect performance by algorithms; this can even occur in situations where the algorithm may outperform humans in certain accuracy measures (Dietvorst et al., 2016; Prahl and Van Swol, 2017).”