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

Yunjin Tong· July 2, 2026 View original

Summary

This paper studies human oversight of AI agents in a contextual-bandit game where both human and AI have private information, modeling scenarios where an AI inspects a situation its supervisor cannot. It characterizes optimal and myopic oversight rules, revealing a "slab of avoidable harm" due to non-credible communication.

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.

Why it matters

For professionals designing or managing AI systems that require human oversight, understanding the implications of two-sided informational asymmetry is critical for building trustworthy systems and effective human-AI collaboration, especially in high-stakes environments.

How to implement this in your domain

  1. 1Design AI systems with transparent mechanisms for communicating uncertainty or potential harm to human supervisors.
  2. 2Develop training protocols for human operators that account for the AI's private information and potential for non-credible communication.
  3. 3Implement feedback loops that allow both human and AI to learn from past oversight decisions.
  4. 4Explore methods for AI to actively signal its confidence or concerns in a verifiable manner.

Who benefits

Autonomous SystemsRoboticsDefenseHealthcareFinance

Key takeaways

  • Human-AI oversight is complex with private information on both sides.
  • AI may know an action is harmful, but human oversight might fail due to asymmetry.
  • "Slab of avoidable harm" arises from non-credible oversight communication.
  • Dynamic learning and signaling can help resolve this asymmetry over time.

Original post by Yunjin Tong

"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…"

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