Handling trust is one of the core requirements for facilitating effective interaction between the human and the AI agent. Thus, any decision-making framework designed to work with humans must possess the ability to estimate and leverage human trust. In this paper, we propose a mental model based theory of trust that not only can be used to infer trust, thus providing an alternative to psychological or behavioral trust inference methods, but also can be used as a foundation for any trust-aware decision-making frameworks. First, we introduce what trust means according to our theory and then use the theory to define trust evolution, human reliance and decision making, and a formalization of the appropriate level of trust in the agent. Using human subject studies, we compare our theory against one of the most common trust scales (Muir scale) to evaluate 1) whether the observations from the human studies match our proposed theory and 2) what aspects of trust are more aligned with our proposed theory.

翻译：处理信任是促进人类与AI代理有效交互的核心需求之一。因此，任何旨在与人类协作的决策框架都必须具备估算和利用人类信任的能力。本文提出一种基于心智模型的信任理论，该理论不仅可用于推断信任（从而为心理学或行为学信任推断方法提供替代方案），还能作为任何信任感知决策框架的基础。首先，我们根据该理论定义了信任的含义，进而利用该理论阐述了信任演化、人类依赖与决策过程，并形式化了对AI代理的适当信任水平。通过人类受试者研究，我们将该理论与最常用的信任量表之一（Muir量表）进行比较，以评估：1）人类研究的观测结果是否与所提出的理论一致；2）信任的哪些方面与该理论更加贴合。