Trust is essential for our interactions with others but also with artificial intelligence (AI) based systems. To understand whether a user trusts an AI, researchers need reliable measurement tools. However, currently discussed markers mostly rely on expensive and invasive sensors, like electroencephalograms, which may cause discomfort. The analysis of mouse trajectory has been suggested as a convenient tool for trust assessment. However, the relationship between trust, confidence and mouse trajectory is not yet fully understood. To provide more insights into this relationship, we asked participants (n = 146) to rate whether several tweets were offensive while an AI suggested its assessment. Our results reveal which aspects of the mouse trajectory are affected by the users subjective trust and confidence ratings; yet they indicate that these measures might not explain sufficiently the variance to be used on their own. This work examines a potential low-cost trust assessment in AI systems.

翻译：信任对于我们与他人以及基于人工智能（AI）系统的互动至关重要。为了理解用户是否信任AI，研究人员需要可靠的测量工具。然而，当前讨论的标记大多依赖于昂贵且侵入性的传感器（如脑电图），这可能会引起不适。鼠标轨迹分析已被提出作为信任评估的便捷工具。然而，信任、置信度与鼠标轨迹之间的关系尚未完全明确。为了深入探究这一关系，我们邀请参与者（n = 146）在AI提供评估建议的情况下，判断多条推文是否具有攻击性。我们的结果揭示了用户主观信任与置信度评分对鼠标轨迹哪些方面产生影响；同时表明，这些测量指标可能不足以单独解释足够的方差。本研究探讨了一种潜在的低成本AI系统信任评估方法。