Author
Listed:
- Mengchen Dong
(Max Planck Institute for Human Development - Max-Planck-Gesellschaft)
- Levin Brinkmann
(Max Planck Institute for Human Development - Max-Planck-Gesellschaft)
- Omar Sherif
(Max Planck Institute for Human Development - Max-Planck-Gesellschaft)
- Shihan Wang
(Universiteit Utrecht / Utrecht University [Utrecht])
- Xinyu Zhang
(Universiteit Utrecht / Utrecht University [Utrecht])
- 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, TSM - Toulouse School of Management Research - UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - CNRS - Centre National de la Recherche Scientifique - TSM - Toulouse School of Management - UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements de Toulouse)
- Iyad Rahwan
(Max Planck Institute for Human Development - Max-Planck-Gesellschaft)
Abstract
Experimental evidence on worker responses to AI management remains mixed, partly due to limitations in experimental fidelity. We address these limitations with a customized workplace in the Minecraft platform, enabling high-resolution behavioral tracking of autonomous task execution, and ensuring that participants approach the task with well-formed expectations about their own competence. Workers (N = 382) completed repeated production tasks under either human, AI, or hybrid management. An AI manager trained on humandefined evaluation principles systematically assigned lower performance ratings and reduced wages by 40%, without adverse effects on worker motivation and sense of fairness. These effects were driven by a muted emotional response to AI evaluation, compared to evaluation by a human. The very features that make AI appear impartial may also facilitate silent exploitation, by suppressing the social reactions that normally constrain extractive practices in human-managed work.
Suggested Citation
Mengchen Dong & Levin Brinkmann & Omar Sherif & Shihan Wang & Xinyu Zhang & Jean-François Bonnefon & Iyad Rahwan, 2025.
"Experimental Evidence That AI-Managed Workers Tolerate Lower Pay Without Demotivation,"
Working Papers
hal-05229276, HAL.
Handle:
RePEc:hal:wpaper:hal-05229276
Note: View the original document on HAL open archive server: https://hal.science/hal-05229276v1
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