Experimental Evidence That AI-Managed Workers Tolerate Lower Pay Without Demotivation

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 human-defined 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.

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Publication Details

Journal
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Published
2025-10-15
DOI
https://doi.org/10.1609/aies.v8i1.36590
Primary Topic
Impact of AI and Big Data on Business and Society
Type
article
Field-Weighted Citation Impact
0.00
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article

Experimental Evidence That AI-Managed Workers Tolerate Lower Pay Without Demotivation

Iyad Rahwan, Jean‐François Bonnefon, Mengchen Dong, Levin Brinkmann et al.
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Impact of AI and Big Data on Business and Society
article

Experimental Evidence That AI-Managed Workers Tolerate Lower Pay Without Demotivation

Iyad Rahwan, Jean‐François Bonnefon, Mengchen Dong, Levin Brinkmann, Xinyu Zhang, Shihan Wang, Omar Sherif
article en

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 human-defined 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.

Proceedings of the AAAI/ACM Conference on AI Ethics and SocietyVol. 8(1)
Utrecht University (NL), Toulouse School of Economics (FR), Max Planck Institute for Human Development (DE), Technische Universität Berlin (DE)
Openalex Percentile: Top 66%
Impact of AI and Big Data on Business and Society
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Experimental Evidence That AI-Managed Workers Tolerate Lower Pay Without Demotivation — Iyad Rahwan, Jean‐François Bonnefon, et al. · Proceedings of the AAAI/ACM Conference on AI Ethics and Society (2025) | TGRS Research Map | TGRS