基于动态结构方程模型（Dynamic SEM）预测人机协作中的信任动态
Predicting Trust Dynamics with Dynamic SEM in Human-AI Cooperation
AI总结：
针对人机协作中信任动态难以直接观测的问题，提出基于动态结构方程模型的预测方法，通过构建并优化变量因果路径图，在无人机和自动驾驶任务中分别实现90%和99%的过度/不足信任预测准确率，性能优于传统自回归方法。
摘要：
人类对人工智能（AI）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、人机交互等AI技术应用场景中尤为重要。信任能推动AI系统的合理使用，从而最大化其潜在效益；若人类对AI过度信任或信任不足，则会引发误用、事故等严重问题。为预防这类情况，需预测信任动态，但信任是人类内部状态，难以直接观测。因此，我们提出一种基于动态结构方程模型（Dynamic SEM）的信任动态预测模型，该模型是能处理时序数据的SEM扩展。我们以探索方式构建了变量间因果关系的路径图，并优化路径结构以提升有效性。在无人机模拟器任务中，该模型预测过度/不足信任的准确率达90%；在自动驾驶任务中，准确率达99%。结果表明，该方法性能优于包括自回归家族在内的传统方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
