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[2510.26752] The Oversight Game: Learning to Cooperatively Balance an AI Agent's Safety and Autonomy
Summary
The paper explores a framework for balancing AI agent autonomy and human oversight through a cooperative game model, ensuring safety without altering the system.
Why It Matters
As AI systems become more autonomous, maintaining human oversight is crucial for safety. This research proposes a structured approach to align AI behavior with human values, addressing significant concerns in AI safety and ethics.
Key Takeaways
- Introduces a two-player Markov game model for AI-human interaction.
- Proves an alignment guarantee that enhances both AI autonomy and human welfare.
- Demonstrates practical applications through simulations and real-world tasks.
- Encourages transparent control mechanisms for safer AI operations.
- Addresses critical safety challenges in deploying advanced AI systems.
Computer Science > Artificial Intelligence arXiv:2510.26752 (cs) [Submitted on 30 Oct 2025 (v1), last revised 19 Feb 2026 (this version, v2)] Title:The Oversight Game: Learning to Cooperatively Balance an AI Agent's Safety and Autonomy Authors:William Overman, Mohsen Bayati View a PDF of the paper titled The Oversight Game: Learning to Cooperatively Balance an AI Agent's Safety and Autonomy, by William Overman and 1 other authors View PDF HTML (experimental) Abstract:As increasingly capable agents are deployed, a central safety challenge is how to retain meaningful human control without modifying the underlying system. We study a minimal control interface in which an agent chooses whether to act autonomously (play) or defer (ask), while a human simultaneously chooses whether to be permissive (trust) or engage in oversight (oversee), and model this interaction as a two-player Markov game. When this game forms a Markov Potential Game, we prove an alignment guarantee: any increase in the agent's utility from acting more autonomously cannot decrease the human's value. This establishes a form of intrinsic alignment where the agent's incentive to seek autonomy is structurally coupled to the human's welfare. Practically, the framework induces a transparent control layer that encourages the agent to defer when risky and act when safe. While we use gridworld simulations to illustrate the emergence of this collaboration, our primary validation involves an agentic tool-use task in wh...
