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Bad machines corrupt good morals

Author

Listed:
  • Nils Köbis

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

  • 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

Machines powered by Artificial Intelligence (AI) are now influencing the behavior of humans in ways that are both like and unlike the ways humans influence each other. In light of recent research showing that other humans can exert a strong corrupting influence on people's ethical behavior, worry emerges about the corrupting power of AI agents. To estimate the empirical validity of these fears, we review the available evidence from behavioral science, human-computer interaction, and AI research. We propose that the main social roles through which both humans and machines can influence ethical behavior are (a) role model, (b) advisor,(c) partner, and (d) delegate. When AI agents become influencers (role models or advisors), their corrupting power may not exceed (yet) the corrupting power of humans. However, AI agents acting as enablers of unethical behavior (partners or delegates) have many characteristics that may let people reap unethical benefits while feeling good about themselves, indicating good reasons for worry. Based on these insights, we outline a research agenda that aims at providing more behavioral insights for better AI oversight.

Suggested Citation

  • Nils Köbis & Jean-François Bonnefon & Iyad Rahwan, 2023. "Bad machines corrupt good morals," Working Papers hal-04164419, HAL.
    • Handle: RePEc:hal:wpaper:hal-04164419
    Note: View the original document on HAL open archive server: https://hal.science/hal-04164419
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    Cited by:

    1. Werner, Tobias, 2021. "Algorithmic and human collusion," DICE Discussion Papers 372, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    2. Leib, Margarita & Köbis, Nils & Rilke, Rainer Michael & Hagens, Marloes & Irlenbusch, Bernd, 2023. "Corrupted by Algorithms? How AI-Generated and Human-Written Advice Shape (Dis)Honesty," IZA Discussion Papers 16293, Institute of Labor Economics (IZA).
    3. von Schenk, Alicia & Klockmann, Victor & Bonnefon, Jean-François & Rahwan, Iyad & Köbis, Nils, 2023. "Lie-detection algorithms attract few users but vastly increase accusation rates," IAST Working Papers 23-155, Institute for Advanced Study in Toulouse (IAST).
    4. Chugunova, Marina & Sele, Daniela, 2022. "We and It: An interdisciplinary review of the experimental evidence on how humans interact with machines," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 99(C).
    5. Lukas Lanz & Roman Briker & Fabiola H. Gerpott, 2024. "Employees Adhere More to Unethical Instructions from Human Than AI Supervisors: Complementing Experimental Evidence with Machine Learning," Journal of Business Ethics, Springer, vol. 189(3), pages 625-646, January.
    6. Alicia von Schenk & Victor Klockmann & Jean-Franc{c}ois Bonnefon & Iyad Rahwan & Nils Kobis, 2022. "Lie detection algorithms attract few users but vastly increase accusation rates," Papers 2212.04277, arXiv.org.
    7. Lechardoy, Lucie & López Forés, Laura & Codagnone, Cristiano, 2023. "Artificial intelligence at the workplace and the impacts on work organisation, working conditions and ethics," 32nd European Regional ITS Conference, Madrid 2023: Realising the digital decade in the European Union – Easier said than done? 277997, International Telecommunications Society (ITS).

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