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.

AI 系统的关键是提供用户可以理解和采取行动的解释。本论文通过对认知科学和解释相关的文献研究，以及调查人们对自主驾驶车辆行为解释的生成和评价，发现人们倾向于用目的论解释决策和行为，无论是对于其他人还是自主驾驶车辆都表现出这一倾向。研究结果促进了对于自主驾驶决策的人工注释视频情况和解释数据集 (HEADD) 的进一步研究。