Cognitive science can help us understand which explanations people might expect, and in which format they frame these explanations, whether causal, counterfactual, or teleological (i.e., purpose-oriented). Understanding the relevance of these concepts is crucial for building good explainable AI (XAI) which offers recourse and actionability. Focusing on autonomous driving, a complex decision-making domain, we report empirical data from two surveys on (i) how people explain the behavior of autonomous vehicles in 14 unique scenarios (N1=54), and (ii) how they perceive these explanations in terms of complexity, quality, and trustworthiness (N2=356). Participants deemed teleological explanations significantly better quality than counterfactual ones, with perceived teleology being the best predictor of perceived quality and trustworthiness. Neither the perceived teleology nor the quality were affected by whether the car was an autonomous vehicle or driven by a person. This indicates that people use teleology to evaluate information about not just other people but also autonomous vehicles. Taken together, our findings highlight the importance of explanations that are framed in terms of purpose rather than just, as is standard in XAI, the causal mechanisms involved. We release the 14 scenarios and more than 1,300 elicited explanations publicly as the Human Explanations for Autonomous Driving Decisions (HEADD) dataset.

翻译：认知科学有助于理解人们可能期待何种解释，以及他们以何种框架（因果性、反事实性或目的论）构建这些解释。理解这些概念的相关性对于构建能够提供补救措施和可操作性的优秀可解释人工智能（XAI）至关重要。聚焦于自动驾驶这一复杂决策领域，我们报告了两项调查的实证数据：(i) 人们在14种独特场景中如何解释自动驾驶车辆的行为（N1=54），(ii) 他们如何从复杂度、质量和可信度角度评价这些解释（N2=356）。参与者认为目的论解释的质量显著优于反事实解释，其中感知目的性是预测感知质量和可信度的最佳指标。无论是自动驾驶车辆还是人类驾驶的车辆，感知目的性及质量均未受影响。这表明人们不仅将目的性用于评估他人信息，也用于评估自动驾驶车辆。综合而言，我们的发现凸显了以目的为导向的解释的重要性，而不仅仅是像XAI标准中所采用的因果机制。我们公开了14种场景及超过1,300条引发的解释，形成自动驾驶决策的人类解释（HEADD）数据集。