ResearcharXiv cs.AI
A Contextual-Bandit Oversight Game with Two-Sided Informational Asymmetry
#ai oversight#contextual bandit#information asymmetry#reinforcement learning#game theory
English
The paper explores a contextual-bandit oversight game characterized by two-sided informational asymmetry, where humans know their reward functions and AI knows the quality of its proposed actions. It introduces a framework that highlights the gap between optimal team behavior and myopic human oversight, illustrating the implications of non-credible communication in AI oversight scenarios.
中文
本文探讨了一个具有双向信息不对称的上下文赌博监督游戏，其中人类知道他们的奖励函数，而人工智能知道其建议行为的质量。它引入了一个框架，突出了最佳团队行为与短视人类监督之间的差距，说明了在人工智能监督场景中非可信沟通的影响。