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Lie-detection algorithms attract few users but vastly increase accusation rates

Author

Listed:
  • Alicia von Schenk

    (JMU - Julius-Maximilians-Universität Würzburg = University of Würzburg [Würsburg, Germany], Max Planck Institute for Human Development - Max-Planck-Gesellschaft)

  • Victor Klockmann

    (Max Planck Institute for Human Development - Max-Planck-Gesellschaft, JMU - Julius-Maximilians-Universität Würzburg = University of Würzburg [Würsburg, Germany])

  • Jean-François Bonnefon

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements 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)

  • Iyad Rahwan

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

  • Nils Köbis

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

Abstract

People are not very good at detecting lies, which may explain why they refrain from accusing others of lying, given the social costs attached to false accusations — both for the accuser and the accused. Here we consider how this social balance might be disrupted by the availability of lie-detection algorithms powered by Artificial Intelligence (AI). Will people elect to use lie-detection AI that outperforms humans, and if so, will they show less restraint in their accusations? To find out, we built a machine learning classifier whose accuracy (66.86%) was significantly better than human accuracy (46.47%) lie-detection task. We conducted an incentivized lie-detection experiment (N = 2040) in which we measured participants' propensity to use the algorithm, as well as the impact of that use on accusation rates and accuracy. Our results reveal that (a) requesting predictions from the lie-detection AI and especially (b) receiving AI predictions that accuse others of lying increase accusation rates. Due to the low uptake of the algorithm (31.76% requests), we do not see an improvement in accuracy when the AI prediction becomes available for purchase.

Suggested Citation

  • Alicia von Schenk & Victor Klockmann & Jean-François Bonnefon & Iyad Rahwan & Nils Köbis, 2023. "Lie-detection algorithms attract few users but vastly increase accusation rates," Working Papers hal-04191443, HAL.
    • Handle: RePEc:hal:wpaper:hal-04191443
    Note: View the original document on HAL open archive server: https://hal.science/hal-04191443v1
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