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

Author

Listed:
  • Ibrahim Filiz
  • Jan René Judek
  • Marco Lorenz
  • Markus Spiwoks

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.

Suggested Citation

  • Ibrahim Filiz & Jan René Judek & Marco Lorenz & Markus Spiwoks, 2023. "The extent of algorithm aversion in decision-making situations with varying gravity," PLOS ONE, Public Library of Science, vol. 18(2), pages 1-21, February.
    • Handle: RePEc:plo:pone00:0278751
    DOI: 10.1371/journal.pone.0278751
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    References listed on IDEAS

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    1. Azim Shariff & Jean-François Bonnefon & Iyad Rahwan, 2017. "Psychological roadblocks to the adoption of self-driving vehicles," Nature Human Behaviour, Nature, vol. 1(10), pages 694-696, October.
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    Cited by:

    1. DiGiuseppe, Matthew & Paula, Katrin & Rommel, Tobias, 2025. "Ad Machina: Partisanship and Support for Delegating Government Decisions to Autonomous Algorithms," SocArXiv rnj5h_v2, Center for Open Science.

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