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How safe is safe enough? Psychological mechanisms underlying extreme safety demands for self-driving cars

Author

Listed:
  • Azim Shariff

    (UBC - University of British Columbia)

  • Jean-François Bonnefon

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CNRS - Centre National de la Recherche Scientifique)

  • Iyad Rahwan

    (Max Planck Institute for Human Development - Max-Planck-Gesellschaft)

Abstract

Autonomous Vehicles (AVs) promise of a multi-trillion-dollar industry that revolutionizes transportation safety and convenience depends as much on overcoming the psychological barriers to their widespread use as the technological and legal challenges. The first AV-related traffic fatalities have pushed manufacturers and regulators towards decisions about how mature AV technology should be before the cars are rolled out in large numbers. We discuss the psychological factors underlying the question of how safe AVs need to be to compel consumers away from relying on the abilities of human drivers. For consumers, how safe is safe enough? Three preregistered studies (N = 4,566) reveal that the established psychological biases of algorithm aversion and the better-than-average effect leave consumers averse to adopting AVs unless the cars meet extremely potentially unrealistically high safety standards. Moreover, these biases prove stubbornly hard to overcome, and risk substantially delaying the adoption of life-saving autonomous driving technology. We end by proposing that, from a psychological perspective, the emphasis AV advocates have put on safety may be misplaced.

Suggested Citation

  • Azim Shariff & Jean-François Bonnefon & Iyad Rahwan, 2021. "How safe is safe enough? Psychological mechanisms underlying extreme safety demands for self-driving cars," Post-Print hal-03236635, HAL.
  • Handle: RePEc:hal:journl:hal-03236635
    DOI: 10.1016/j.trc.2021.103069
    Note: View the original document on HAL open archive server: https://hal.science/hal-03236635
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    References listed on IDEAS

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    Cited by:

    1. Xing, Yingying & Zhou, Huiyu & Han, Xiao & Zhang, Meng & Lu, Jian, 2022. "What influences vulnerable road users’ perceptions of autonomous vehicles? A comparative analysis of the 2017 and 2019 Pittsburgh surveys," Technological Forecasting and Social Change, Elsevier, vol. 176(C).

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    Keywords

    Autonomous vehicles; Automation; Algorithm aversion; Safety; Illusory superiority;
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