The nature of explanations provided by an explainable AI algorithm has been a topic of interest in the explainable AI and human-computer interaction community. In this paper, we investigate the effects of natural language explanations' specificity on passengers in autonomous driving. We extended an existing data-driven tree-based explainer algorithm by adding a rule-based option for explanation generation. We generated auditory natural language explanations with different levels of specificity (abstract and specific) and tested these explanations in a within-subject user study (N=39) using an immersive physical driving simulation setup. Our results showed that both abstract and specific explanations had similar positive effects on passengers' perceived safety and the feeling of anxiety. However, the specific explanations influenced the desire of passengers to takeover driving control from the autonomous vehicle (AV), while the abstract explanations did not. We conclude that natural language auditory explanations are useful for passengers in autonomous driving, and their specificity levels could influence how much in-vehicle participants would wish to be in control of the driving activity.

翻译：可解释人工智能算法所提供的解释特性一直是可解释人工智能与人机交互领域关注的重点。本文研究了自然语言解释的特异性对自动驾驶中乘客的影响。我们通过在现有基于数据驱动的树状解释器算法中增加基于规则的选项来生成解释，从而扩展了该算法。我们生成了不同特异性水平（抽象与具体）的听觉自然语言解释，并在沉浸式物理驾驶模拟环境中进行了被试内用户研究（N=39）。结果显示，抽象和具体解释在提升乘客感知安全感和焦虑情绪方面具有相似的积极效果。然而，具体解释会影响乘客从自动驾驶汽车接管驾驶控制的意愿，而抽象解释则没有这种影响。我们得出结论：自然语言听觉解释对自动驾驶中的乘客具有实用价值，且其特异性水平会影响车内参与者对驾驶活动控制权的期望程度。