A hallmark of a good XAI system is explanations that users can understand and act on. In many cases, this requires a system to offer causal or counterfactual explanations that are intelligible. Cognitive science can help us understand what kinds of explanations users might expect, and in which format to frame these explanations. We briefly review relevant literature from the cognitive science of explanation, particularly as it concerns teleology, the tendency to explain a decision in terms of the purpose it was meant to achieve. We then report empirical data on how people generate explanations for the behavior of autonomous vehicles, and how they evaluate these explanations. In a first survey, participants (n=54) were shown videos of a road scene and asked to generate either mechanistic, counterfactual, or teleological verbal explanations for a vehicle's actions. In the second survey, a different set of participants (n=356) rated these explanations along various metrics including quality, trustworthiness, and how much each explanatory mode was emphasized in the explanation. Participants deemed mechanistic and teleological explanations as significantly higher quality than counterfactual explanations. In addition, perceived teleology was the best predictor of perceived quality and trustworthiness. Neither perceived teleology nor quality ratings were affected by whether the car whose actions were being explained was an autonomous vehicle or was being driven by a person. The results show people use and value teleological concepts to evaluate information about both other people and autonomous vehicles, indicating they find the 'intentional stance' a convenient abstraction. We make our dataset of annotated video situations with explanations, called Human Explanations for Autonomous Driving Decisions (HEADD), publicly available, which we hope will prompt further research.

翻译：优秀可解释人工智能（XAI）系统的标志在于其能提供用户理解并据此行动的解释。在许多情况下，这要求系统提供可理解的因果或反事实解释。认知科学有助于我们理解用户可能期待何种解释类型，以及如何构建这些解释的框架。我们简要回顾了解释的认知科学相关文献，特别是涉及目的论（即倾向于根据决策意图解释行为）的研究。随后，我们报告了关于人们如何生成对自动驾驶汽车行为的解释，以及如何评估这些解释的实证数据。在第一项调查中，参与者（n=54）观看了道路场景视频，并被要求为车辆行为生成机械论、反事实或目的论的口头解释。第二项调查中，另一组参与者（n=356）根据包括解释质量、可信度以及每种解释模式在解释中的强调程度等多个指标对这些解释进行评分。参与者认为机械论和目的论解释的质量显著高于反事实解释。此外，感知到的目的论是解释质量和可信度的最佳预测因子。无论被解释行为的车辆是自动驾驶汽车还是由人驾驶，感知到的目的论和质量评分均未受影响。结果表明，人们在评估关于他人和自动驾驶汽车的信息时，会使用并重视目的论概念，这表明他们认为“意向立场”是一种便捷的抽象工具。我们将包含视频情境注释解释的数据集——称为“自动驾驶决策的人类解释”（HEADD）公开提供，以期推动进一步研究。