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Preventing algorithm aversion: People are willing to use algorithms with a learning label
Chacon,Alvaro Kausel,Edgar E. Reyes,Tomas Trautmann,Stefan
Chacon,Alvaro
Kausel,Edgar E.
Reyes,Tomas
Trautmann,Stefan
Abstract
As algorithms often outperform humans in prediction, algorithm aversion is economically harmful. To enhance algorithm utilization, we suggest emphasizing their learning capabilities, i.e., their increasing predictive precision over time, through the explicit addition of a "learning" label. We conducted five incentivized studies in which 1,167 participants may prefer algorithms or take up algorithmic advice in a financial or healthcare related task. Our results suggest that people use algorithms with a learning label to a greater extent than algorithms without such a label. As the accuracy of advice improves beyond a threshold, the use of algorithms with a learning label increases more than algorithms without a label. Thus, we show that a salient learning attribute can positively affect algorithm use in both the financial and health domain.
Description
Publisher Copyright: © 2024 Elsevier Inc.
Date
2025-01
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Keywords
Advice, Algorithm appreciation, Algorithm aversion, Algorithm use, Learning algorithms
Citation
Chacon, A, Kausel, E E, Reyes, T & Trautmann, S 2025, 'Preventing algorithm aversion : People are willing to use algorithms with a learning label', Journal of Business Research, vol. 187, 115032. https://doi.org/10.1016/j.jbusres.2024.115032
License
info:eu-repo/semantics/openAccess