Humans' trust in AI constitutes a pivotal element in fostering a synergistic relationship between humans and AI. This is particularly significant in the context of systems that leverage AI technology, such as autonomous driving systems and human-robot interaction. Trust facilitates appropriate utilization of these systems, thereby optimizing their potential benefits. If humans over-trust or under-trust an AI, serious problems such as misuse and accidents occur. To prevent over/under-trust, it is necessary to predict trust dynamics. However, trust is an internal state of humans and hard to directly observe. Therefore, we propose a prediction model for trust dynamics using dynamic structure equation modeling, which extends SEM that can handle time-series data. A path diagram, which shows causalities between variables, is developed in an exploratory way and the resultant path diagram is optimized for effective path structures. Over/under-trust was predicted with 90\% accuracy in a drone simulator task,, and it was predicted with 99\% accuracy in an autonomous driving task. These results show that our proposed method outperformed the conventional method including an auto regression family.

翻译：人类对人工智能的信任是促进人机协同关系的关键要素。这在自动驾驶系统和人机交互等利用人工智能技术的系统背景下尤为重要。信任有助于这些系统的合理使用，从而优化其潜在效益。若人类对人工智能过度信任或信任不足，则会导致误用和事故等严重问题。为防止信任失衡，有必要预测信任动态。然而，信任作为人类的内部状态难以直接观测。为此，我们提出一种基于动态结构方程模型的信任动态预测方法，该方法扩展了能够处理时间序列数据的结构方程模型。我们以探索性方式构建了展示变量间因果关系的路径图，并针对有效路径结构对结果路径图进行了优化。在无人机模拟任务中，信任失衡预测准确率达到90%；在自动驾驶任务中，预测准确率高达99%。这些结果表明，我们所提出的方法优于包括自回归模型系列在内的传统方法。