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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.21752 |
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| _version_ | 1866913862399295488 |
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| author | Dong, Mengchen Brinkmann, Levin Sherif, Omar Wang, Shihan Zhang, Xinyu Bonnefon, Jean-François Rahwan, Iyad |
| author_facet | Dong, Mengchen Brinkmann, Levin Sherif, Omar Wang, Shihan Zhang, Xinyu Bonnefon, Jean-François Rahwan, Iyad |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_21752 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Experimental Evidence That AI-Managed Workers Tolerate Lower Pay Without Demotivation Dong, Mengchen Brinkmann, Levin Sherif, Omar Wang, Shihan Zhang, Xinyu Bonnefon, Jean-François Rahwan, Iyad Computers and Society Human-Computer Interaction 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. |
| title | Experimental Evidence That AI-Managed Workers Tolerate Lower Pay Without Demotivation |
| topic | Computers and Society Human-Computer Interaction |
| url | https://arxiv.org/abs/2505.21752 |