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

May 27, 2025 Β· Declared Dead Β· πŸ› Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society

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Authors Mengchen Dong, Levin Brinkmann, Omar Sherif, Shihan Wang, Xinyu Zhang, Jean-FranΓ§ois Bonnefon, Iyad Rahwan arXiv ID 2505.21752 Category cs.CY: Computers & Society Cross-listed cs.HC Citations 0 Venue Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society Last Checked 5 months ago
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.
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