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Bad machines corrupt good morals

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  • Köbis, Nils
  • Bonnefon, Jean-François
  • Rahwan, Iyad

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

  • Köbis, Nils & Bonnefon, Jean-François & Rahwan, Iyad, 2021. "Bad machines corrupt good morals," TSE Working Papers 21-1212, Toulouse School of Economics (TSE).
    • Handle: RePEc:tse:wpaper:125602
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    1. Zhixin Dai & Fabio Galeotti & Marie Claire Villeval, 2018. "Cheating in the Lab Predicts Fraud in the Field: An Experiment in Public Transportation," Management Science, INFORMS, vol. 64(3), pages 1081-1100, March.
    2. Jacob W. Crandall & Mayada Oudah & Tennom & Fatimah Ishowo-Oloko & Sherief Abdallah & Jean-François Bonnefon & Manuel Cebrian & Azim Shariff & Michael A. Goodrich & Iyad Rahwan, 2018. "Cooperating with machines," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
      • Abdallah, Sherief & Bonnefon, Jean-François & Cebrian, Manuel & Crandall, Jacob W. & Ishowo-Oloko, Fatimah & Oudah, Mayada & Rahwan, Iyad & Shariff, Azim & Tennom,, 2017. "Cooperating with Machines," TSE Working Papers 17-806, Toulouse School of Economics (TSE).
      • Abdallah, Sherief & Bonnefon, Jean-François & Cebrian, Manuel & Crandall, Jacob W. & Ishowo-Oloko, Fatimah & Oudah, Mayada & Rahwan, Iyad & Shariff, Azim & Tennom,, 2017. "Cooperating with Machines," IAST Working Papers 17-68, Institute for Advanced Study in Toulouse (IAST).
      • Jacob Crandall & Mayada Oudah & Fatimah Ishowo-Oloko Tennom & Fatimah Ishowo-Oloko & Sherief Abdallah & Jean-François Bonnefon & Manuel Cebrian & Azim Shariff & Michael Goodrich & Iyad Rahwan, 2018. "Cooperating with machines," Post-Print hal-01897802, HAL.
    3. Kirchkamp, Oliver & Strobel, Christina, 2019. "Sharing responsibility with a machine," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 80(C), pages 25-33.
    4. Schniter, E. & Shields, T.W. & Sznycer, D., 2020. "Trust in humans and robots: Economically similar but emotionally different," Journal of Economic Psychology, Elsevier, vol. 78(C).
    5. Kate Crawford, 2019. "Halt the use of facial-recognition technology until it is regulated," Nature, Nature, vol. 572(7771), pages 565-565, August.
    6. Zoe Rahwan & Erez Yoeli & Barbara Fasolo, 2019. "Heterogeneity in banker culture and its influence on dishonesty," Nature, Nature, vol. 575(7782), pages 345-349, November.
    7. Mark V. Pezzo & Stephanie P. Pezzo, 2006. "Physician Evaluation after Medical Errors: Does Having a Computer Decision Aid Help or Hurt in Hindsight?," Medical Decision Making, , vol. 26(1), pages 48-56, January.
    8. Alain Cohn & Michel André Maréchal, 2018. "Laboratory Measure of Cheating Predicts School Misconduct," Economic Journal, Royal Economic Society, vol. 128(615), pages 2743-2754, November.
    9. On Amir & Dan Ariely & Alan Cooke & David Dunning & Nicholas Epley & Uri Gneezy & Botond Koszegi & Donald Lichtenstein & Nina Mazar & Sendhil Mullainathan & Drazen Prelec & Eldar Shafir & Jose Silva, 2005. "Psychology, Behavioral Economics, and Public Policy," Marketing Letters, Springer, vol. 16(3), pages 443-454, December.
    10. Andreas Ostermaier & Matthias Uhl, 2017. "Spot on for liars! How public scrutiny influences ethical behavior," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-11, July.
    11. Simon Gächter & Jonathan F. Schulz, 2016. "Intrinsic honesty and the prevalence of rule violations across societies," Nature, Nature, vol. 531(7595), pages 496-499, March.
    12. Daniel Houser & Robert Kurzban, 2002. "Revisiting Kindness and Confusion in Public Goods Experiments," American Economic Review, American Economic Association, vol. 92(4), pages 1062-1069, September.
    13. Dan Ariely & Nina Mazar, 2006. "Dishonesty in everyday life and its policy implications," Working Papers 06-3, Federal Reserve Bank of Boston.
    14. Charles Efferson & Sonja Vogt & Ernst Fehr, 2020. "The promise and the peril of using social influence to reverse harmful traditions," Nature Human Behaviour, Nature, vol. 4(1), pages 55-68, January.
    15. Kate Crawford & Ryan Calo, 2016. "There is a blind spot in AI research," Nature, Nature, vol. 538(7625), pages 311-313, October.
    16. Daniel Paravisini & Antoinette Schoar, 2013. "The Incentive Effect of Scores: Randomized Evidence from Credit Committees," NBER Working Papers 19303, National Bureau of Economic Research, Inc.
    17. Raymond Fisman & Edward Miguel, 2007. "Corruption, Norms, and Legal Enforcement: Evidence from Diplomatic Parking Tickets," Journal of Political Economy, University of Chicago Press, vol. 115(6), pages 1020-1048, December.
    18. Francesca Gino & Don A. Moore & Max H. Bazerman, 2008. "No harm, no foul: The outcome bias in ethical judgments," Harvard Business School Working Papers 08-080, Harvard Business School, revised Apr 2009.
    19. Wiltermuth, Scott S., 2011. "Cheating more when the spoils are split," Organizational Behavior and Human Decision Processes, Elsevier, vol. 115(2), pages 157-168, July.
    20. Heiko Rauhut, 2013. "Beliefs about Lying and Spreading of Dishonesty: Undetected Lies and Their Constructive and Destructive Social Dynamics in Dice Experiments," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-8, November.
    21. Allison Koenecke & Andrew Nam & Emily Lake & Joe Nudell & Minnie Quartey & Zion Mengesha & Connor Toups & John R. Rickford & Dan Jurafsky & Sharad Goel, 2020. "Racial disparities in automated speech recognition," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(14), pages 7684-7689, April.
    22. Mikhail Drugov & John Hamman & Danila Serra, 2014. "Intermediaries in corruption: an experiment," Experimental Economics, Springer;Economic Science Association, vol. 17(1), pages 78-99, March.
    23. Margarita Leib & Nils C. Kobis & Rainer Michael Rilke & Marloes Hagens & Bernd Irlenbusch, 2021. "The corruptive force of AI-generated advice," Papers 2102.07536, arXiv.org.
    24. Gogoll, Jan & Uhl, Matthias, 2018. "Rage against the machine: Automation in the moral domain," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 74(C), pages 97-103.
    25. Soraperra, Ivan & Weisel, Ori & Zultan, Ro’i & Kochavi, Sys & Leib, Margarita & Shalev, Hadar & Shalvi, Shaul, 2017. "The bad consequences of teamwork," Economics Letters, Elsevier, vol. 160(C), pages 12-15.
    26. Alina Mungiu-Pippidi, 2017. "The time has come for evidence-based anticorruption," Nature Human Behaviour, Nature, vol. 1(1), pages 1-3, January.
    27. Fisman, Ray & Golden, Miriam A., 2017. "Corruption: What Everyone Needs to Know," OUP Catalogue, Oxford University Press, number 9780190463977.
    28. Ostermaier, Andreas & Uhl, Matthias, 2017. "Spot On For Liars! How Public Scrutiny Influences Ethical Behavior," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168167, Verein für Socialpolitik / German Economic Association.
    29. Jason Dana & Roberto Weber & Jason Kuang, 2007. "Exploiting moral wiggle room: experiments demonstrating an illusory preference for fairness," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 33(1), pages 67-80, October.
    30. Rahwan, Zoe & Yoeli, Erez & Fasolo, Barbara, 2019. "Heterogeneity in banker culture and its influence on dishonesty," LSE Research Online Documents on Economics 102656, London School of Economics and Political Science, LSE Library.
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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. 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).
    4. Köbis, Nils & Rahwan, Zoe & Bersch, Clara & Ajaj, Tamer & Bonnefon, Jean-François & Rahwan, Iyad, 2024. "Experimental evidence that delegating to intelligent machines can increase dishonest behaviour," OSF Preprints dnjgz, Center for Open Science.
    5. Elias Fernández Domingos & Inês Terrucha & Rémi Suchon & Jelena Grujić & Juan Burguillo & Francisco Santos & Tom Lenaerts, 2022. "Delegation to artificial agents fosters prosocial behaviors in the collective risk dilemma," Post-Print hal-04296038, HAL.
    6. von Schenk, Alicia & Klockmann, Victor & Bonnefon, Jean-François & Rahwan, Iyad & Köbis, Nils, 2023. "Lie-detection algorithms attract few users but vastly increase accusation rates," TSE Working Papers 23-1448, Toulouse School of Economics (TSE).
    7. 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.
    8. 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.
    9. Emilio Ferrara, 2024. "GenAI against humanity: nefarious applications of generative artificial intelligence and large language models," Journal of Computational Social Science, Springer, vol. 7(1), pages 549-569, April.
    10. 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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    Keywords

    machine behavior; behavioral ethics; corruption; artificial intelligence;
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