The extent of algorithm aversion in decision-making situations with varying gravity

Ibrahim Filiz, Jan René Judek, Marco Lorenz and Markus Spiwoks

PLOS ONE, 2023, vol. 18, issue 2, 1-21

Abstract: Algorithms already carry out many tasks more reliably than human experts. Nevertheless, some subjects have an aversion towards algorithms. In some decision-making situations an error can have serious consequences, in others not. In the context of a framing experiment, we examine the connection between the consequences of a decision-making situation and the frequency of algorithm aversion. This shows that the more serious the consequences of a decision are, the more frequently algorithm aversion occurs. Particularly in the case of very important decisions, algorithm aversion thus leads to a reduction of the probability of success. This can be described as the tragedy of algorithm aversion.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0278751

DOI: 10.1371/journal.pone.0278751

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