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The Social Importance of Being Stubborn When an Organization Meets AI

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  • Foucart, Renaud
  • Zeng, Fanqi
  • Wang, Shidong

Abstract

In the age of Artificial Intelligence (AI), we have access to high-quality advice from intelligent machines, creating tensions between efficiency and autonomy. While it is often individually beneficial to follow AI recommendations, it can lead to dangerous herding behavior when AI provides incorrect information, a phenomenon observed in corporate scandals such as biased hiring algorithms, financial automation, or the monitoring of employees. In this paper, we show how a small group of stubborn workers following their own, imperfect but independent, information may help mitigate the risks of AI advice in their organization. However, stubbornness is costly---such individuals persist only if subsidized in the recruitment and promotion system despite lower average performance, or if they have a strong intrinsic preference for autonomy.

Suggested Citation

  • Foucart, Renaud & Zeng, Fanqi & Wang, Shidong, 2025. "The Social Importance of Being Stubborn When an Organization Meets AI," SocArXiv nfgy3_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:nfgy3_v1
    DOI: 10.31219/osf.io/nfgy3_v1
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    1. Nils Köbis & Zoe Rahwan & Raluca Rilla & Bramantyo Ibrahim Supriyatno & Clara Bersch & Tamer Ajaj & Jean-François Bonnefon & Iyad Rahwan, 2025. "Delegation to artificial intelligence can increase dishonest behaviour," Nature, Nature, vol. 646(8083), pages 126-134, October.
    2. David Owens Jr. & Zachary Grossman Jr. & Ryan Fackler Jr., 2014. "The Control Premium: A Preference for Payoff Autonomy," American Economic Journal: Microeconomics, American Economic Association, vol. 6(4), pages 138-161, November.
    3. Acemoglu, Daron & Ozdaglar, Asuman & ParandehGheibi, Ali, 2010. "Spread of (mis)information in social networks," Games and Economic Behavior, Elsevier, vol. 70(2), pages 194-227, November.
    4. Yulia W. Sullivan & Samuel Fosso Wamba, 2022. "Moral Judgments in the Age of Artificial Intelligence," Journal of Business Ethics, Springer, vol. 178(4), pages 917-943, July.
    5. Bouacida, Elias & Foucart, Renaud & Jalloul, Maya, 2025. "When expert advice fails to reduce the productivity gap: Experimental evidence from chess players," Journal of Economic Behavior & Organization, Elsevier, vol. 236(C).
    6. Erik Brynjolfsson & Danielle Li & Lindsey Raymond, 2025. "Generative AI at Work," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 140(2), pages 889-942.
    7. Björn Bartling & Ernst Fehr & Holger Herz, 2014. "The Intrinsic Value of Decision Rights," Econometrica, Econometric Society, vol. 82, pages 2005-2039, November.
    8. Nils Köbis & Zoe Rahwan & Raluca Rilla & Bramantyo Ibrahim Supriyatno & Clara Bersch & Tamer Ajaj & Jean-François Bonnefon & Iyad Rahwan, 2025. "Delegation to artificial intelligence can increase dishonest behaviour," Nature, Nature, vol. 646(8083), pages 126-134, October.
    9. Jacob K. Goeree & Thomas R. Palfrey & Brian W. Rogers & Richard D. McKelvey, 2007. "Self-Correcting Information Cascades," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 74(3), pages 733-762.
    10. Nils Köbis & Jean-François Bonnefon & Iyad Rahwan, 2021. "Bad machines corrupt good morals," Nature Human Behaviour, Nature, vol. 5(6), pages 679-685, June.
    11. Nikhil Agarwal & Alex Moehring & Pranav Rajpurkar & Tobias Salz, 2023. "Combining Human Expertise with Artificial Intelligence: Experimental Evidence from Radiology," NBER Working Papers 31422, National Bureau of Economic Research, Inc.
    12. John J. Conlon & Malavika Mani & Gautam Rao & Matthew W. Ridley & Frank Schilbach, 2022. "Not Learning from Others," NBER Working Papers 30378, National Bureau of Economic Research, Inc.
    13. Zsófia Tóth & Robert Caruana & Thorsten Gruber & Claudia Loebbecke, 2022. "The Dawn of the AI Robots: Towards a New Framework of AI Robot Accountability," Journal of Business Ethics, Springer, vol. 178(4), pages 895-916, July.
    14. Anderson, Lisa R & Holt, Charles A, 1997. "Information Cascades in the Laboratory," American Economic Review, American Economic Association, vol. 87(5), pages 847-862, December.
    15. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    16. Michelle Vaccaro & Abdullah Almaatouq & Thomas Malone, 2024. "When combinations of humans and AI are useful: A systematic review and meta-analysis," Nature Human Behaviour, Nature, vol. 8(12), pages 2293-2303, December.
    17. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
    18. Sarah Bankins & Paul Formosa, 2023. "The Ethical Implications of Artificial Intelligence (AI) For Meaningful Work," Journal of Business Ethics, Springer, vol. 185(4), pages 725-740, July.
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