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.
Authors
- Iyad Rahwan (ORCID: https://orcid.org/0000-0002-1796-4303)
- Jean‐François Bonnefon (ORCID: https://orcid.org/0000-0002-4959-188X)
- Mengchen Dong (ORCID: https://orcid.org/0000-0001-8547-3808)
- Levin Brinkmann (ORCID: https://orcid.org/0000-0002-1642-8744)
- Xinyu Zhang (ORCID: https://orcid.org/0000-0002-5979-244X)
- Shihan Wang (ORCID: https://orcid.org/0000-0002-0045-9036)
- Omar Sherif
Institutions
- Utrecht University (NL)
- Toulouse School of Economics (FR)
- Max Planck Institute for Human Development (DE)
- Technische Universität Berlin (DE)
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