Research Models Human-AI Oversight with Two-Sided Asymmetry.

The dynamics of human oversight over AI agents become complex when both parties possess private information. This scenario naturally arises when an autonomous AI agent assesses a situation that its human supervisor cannot directly observe. This research delves into such a problem by introducing a contextual-bandit team game that features two-sided informational asymmetry. Building on existing frameworks like Cooperative Inverse Reinforcement Learning, the study simplifies the problem to a one-shot characterization, removing physical state transitions to focus on the core informational challenges. It identifies a "slab of avoidable harm," a region where the AI knows its proposed action is harmful and shutdown would be beneficial, yet a myopic human, relying on prior beliefs, fails to intervene. This gap is attributed to the lack of credible oversight communication and is partially analyzed for its dynamic resolution over repeated interactions through passive learning and active signaling.