arXiv:2607.00155v1 Announce Type: new Abstract: We study runtime human oversight of an AI agent when private information runs in both directions: the human privately knows her reward function, while the AI privately knows the quality of the action it proposes. This is the kind of asymmetry that arises naturally when an autonomous robot or software agent has inspected a situation its human supervisor cannot directly assess. Building on Cooperative Inverse Reinforcement Learning (CIRL) and the Oversight Game, we introduce a contextual-bandit team game with two-sided asymmetric information and a
The proliferation of advanced AI systems necessitates robust mechanisms for human oversight, especially as these agents assume more autonomous roles in complex, real-world environments.
Understanding the dynamics of two-sided informational asymmetry in human-AI interaction is critical for designing trustworthy AI systems and effective governance frameworks.
This research introduces a more nuanced model for AI oversight, acknowledging that both human operators and AI agents possess private, critical information, which can lead to better or worse outcomes.
- · organizations deploying autonomous AI
- · AI safety researchers
- · AI developers focused on transparency and alignment
- · AI systems with opaque internal states
- · human operators without robust feedback mechanisms
Improved theoretical models for human-AI collaboration and oversight.
Development of more sophisticated AI architectures that explicitly account for and communicate informational asymmetry.
Enhanced trust in autonomous AI systems leading to broader adoption in sensitive domains such as defense or critical infrastructure.
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Read at arXiv cs.AI