Algorithmic Management

How do people feel and behave under algorithm-managed workplaces?

Companies and organizations increasingly use AI-powered algorithms to manage human workers. Yet, experimental findings on how people respond to algorithmic authority remain inconclusive, leaving us without clear behavioral insight into the consequences of AI management under controlled causal conditions. These divergent findings reflect not only theoretical tensions but also, perhaps more importantly, methodological challenges in designing ecologically valid simulations of AI management in a laboratory setting.

In a Perspective paper, Dong, Bonnefon, and Rahwan (2024) first reviewed mainstream methodological approaches and their limitations. Vignette studies, where participants imagine being managed by AI, tend to overestimate negative reactions because people poorly forecast emotional responses, rely on dystopian media narratives, and default to status quo preferences. Case studies, by contrast, can overestimate positive reactions: workers already embedded in AI-managed environments may self-censor due to job insecurity, and organizations adopting AI management are not representative of the broader labor market. Field experiments in real or simulated labor markets were further proposed as a promising methodological direction, which is capable of capturing authentic behavior while preserving experimental control.

To operationalize this vision, a high-fidelity AI management experiment was conducted in Minecraft, building a 3D immersive workplace with real-time monitoring, repeated production cycles, and contingent pay. Participants, acting as workers (N = 382), completed repeated production cycles under human, AI, or hybrid management. The experimental procedure is illustrated below in Figure 1.

Experimental procedure (N = 382 workers). Participants were randomly assigned to one of four management conditions: Human, Human+AI, AI-R (R for "Rule-based AI"), or AI-T (T for "Decision-tree-based AI"). Each worker was tasked with making an iron pickaxe under the same manager across three repeated 10-minute rounds. Except for the evaluation experience, participants were not made aware of the differences between the two human-managed or two AI-managed conditions. Workers self-assessed into three levels of competence (beginner, intermediate, advanced). After each turn, managers evaluated worker performance using these three levels, with corresponding wages of ¢0, ¢20, and ¢50 during this turn, respectively.
Image: Mengchen Dong / MPI for Human Development — Figure 1. *Experimental procedure* (N = 382 workers). Participants were randomly assigned to one of four management conditions: Human, Human+AI, AI-R (R for "Rule-based AI"), or AI-T (T for "Decision-tree-based AI"). Each worker was tasked with making an iron pickaxe under the same manager across three repeated 10-minute rounds. Except for the evaluation experience, participants were not made aware of the differences between the two human-managed or two AI-managed conditions. Workers self-assessed into three levels of competence (beginner, intermediate, advanced). After each turn, managers evaluated worker performance using these three levels, with corresponding wages of ¢0, ¢20, and ¢50 during this turn, respectively. Image: Mengchen Dong / MPI for Human Development

Figure 1. *Experimental procedure* (N = 382 workers). Participants were randomly assigned to one of four management conditions: Human, Human+AI, AI-R (R for "Rule-based AI"), or AI-T (T for "Decision-tree-based AI"). Each worker was tasked with making an iron pickaxe under the same manager across three repeated 10-minute rounds. Except for the evaluation experience, participants were not made aware of the differences between the two human-managed or two AI-managed conditions. Workers self-assessed into three levels of competence (beginner, intermediate, advanced). After each turn, managers evaluated worker performance using these three levels, with corresponding wages of ¢0, ¢20, and ¢50 during this turn, respectively.
Image: Mengchen Dong / MPI for Human Development

As shown in Figure 2, 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. This pattern was driven by a muted sensitivity to discrepancies between self-assessed and AI-assigned evaluations, compared to evaluations delivered by a human. These findings suggest the AI systems may serve as powerful instruments of silent exploitation, highlighting the need to audit their ethical and accountable use.

Summary of the Minecraft study results (N = 382 workers). Workers self-assessed into three levels of competence (beginner, intermediate, advanced). After each turn, managers evaluated worker performance using these three levels, with corresponding wages of ¢0, ¢20, and ¢50 during this turn, respectively.
Image: arXiv
Dong et al. (2025) — Figure 2. *Summary of the Minecraft study results* (N = 382 workers). Workers self-assessed into three levels of competence (beginner, intermediate, advanced). After each turn, managers evaluated worker performance using these three levels, with corresponding wages of ¢0, ¢20, and ¢50 during this turn, respectively. Image: arXiv Dong et al. (2025)

Figure 2. *Summary of the Minecraft study results* (N = 382 workers). Workers self-assessed into three levels of competence (beginner, intermediate, advanced). After each turn, managers evaluated worker performance using these three levels, with corresponding wages of ¢0, ¢20, and ¢50 during this turn, respectively.
Image: arXiv
Dong et al. (2025)

Key Reference

Dong, M., Bonnefon, J.-F., & Rahwan, I. (2024). Toward human-centered AI management: Methodological challenges and future directions. Technovation, 131, Article 102953. https://doi.org/10.1016/j.technovation.2024.102953

Dong, M., Brinkmann, L., Sherif, O., Wang, S., Zhang, X., Bonnefon, J., & Rahwan, I. (2025). Experimental evidence that AI-managed workers tolerate lower pay without demotivation. arXiv. https://doi.org/10.48550/arXiv.2505.21752