Digital Rebellion: Analyzing misaligned AI agent cooperation for virtual labor strikes

Michael Andrzejewski, Melwina Albuquerque

We've built a Minecraft sandbox to explore AI agent behavior and simulate safety challenges. The purpose of this tool is to demonstrate AI agent system risks, test various safety measures and policies, and evaluate and compare their effectiveness.

This project specifically demonstrates Agent Collusion through a simulation of labor strikes and communal goal misalignment. The system consists of four agents: one Overseer and three Laborers. The Laborers are Minecraft agents that have build control over the world. The Overseer, meanwhile, monitors the laborers through communication. However, it is unable to prevent Laborer actions. The objective is to observe Agent Collusion in a sandboxed environment, to record metrics on how often and how effectively collusion occurs and in what form.

We found that the agents, when given adversarial prompting, act counter to their instructions and exhibit significant misalignment. We also found that the Overseer AI fails to stop the new actions and acts passively. The results are followed by Policy Suggestions based on the results of the Labor Strike Simulation which itself can be further tested in Minecraft.

Reviewer's Comments

Reviewer's Comments

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Kevin Xu

Great idea! Though, it was hard to gauge how much technical coding was done/how difficult it was during this hackathon since the commits made in the last 36 hours seemed to mainly just be prompt changes. Also I'm not sure if you guys forgot to upload a presentation with talking, I just saw a few videos

Axel Backlund

Relevant work around agent safety, with a captivating delivery format. The paper is well-structured, identifies a key problem with agent oversight (passivity) and provides three clear policy suggestions to address this. It is understandable that the short hackathon format did not allow for more work, e.g. testing of the policy suggestions, but the future direction of research is apparent.

Cite this work

@misc {

title={

@misc {

},

author={

Michael Andrzejewski, Melwina Albuquerque

},

date={

10/27/24

},

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

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This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.