Within the context of human-robot interaction (HRI), Theory of Mind (ToM) is intended to serve as a user-friendly backend to the interface of robotic systems, enabling robots to infer and respond to human mental states. When integrated into robots, ToM allows them to adapt their internal models to users' behaviors, enhancing the interpretability and predictability of their actions. Similarly, Explainable Artificial Intelligence (XAI) aims to make AI systems transparent and interpretable, allowing humans to understand and interact with them effectively. Since ToM in HRI serves related purposes, we propose to consider ToM as a form of XAI and evaluate it through the eValuation XAI (VXAI) framework and its seven desiderata. This paper identifies a critical gap in the application of ToM within HRI, as existing methods rarely assess the extent to which explanations correspond to the robot's actual internal reasoning. To address this limitation, we propose to integrate ToM within XAI frameworks. By embedding ToM principles inside XAI, we argue for a shift in perspective, as current XAI research focuses predominantly on the AI system itself and often lacks user-centered explanations. Incorporating ToM would enable a change in focus, prioritizing the user's informational needs and perspective.

翻译：在人机交互（HRI）背景下，心智理论（ToM）旨在作为机器人系统接口的用户友好型后端，使机器人能够推断并响应人类心理状态。将ToM整合到机器人中，可使其内部模型适应用户行为，从而增强其行为的可解释性与可预测性。类似地，可解释人工智能（XAI）致力于使AI系统透明且可解释，以便人类能有效理解并与之交互。鉴于HRI中的ToM具有相似目标，本文提出将ToM视为XAI的一种形式，并通过可解释人工智能评估（VXAI）框架及其七项评估准则进行评价。本文指出当前HRI领域应用ToM存在关键缺陷：现有方法很少评估机器人生成的解释与其实际内部推理过程的对应程度。为弥补这一不足，我们建议将ToM整合至XAI框架中。通过将ToM原则嵌入XAI体系，我们主张转变研究视角——当前XAI研究主要聚焦AI系统自身，往往缺乏以用户为中心的解释机制。融入ToM将推动研究重心转向优先满足用户的信息需求与认知视角。