人们在解释自动驾驶汽车行为时赋予其目的：认知科学对可解释人工智能的启示
People Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior: Insights from Cognitive Science for Explainable AI
AI总结：
本文基于认知科学提出解释模式框架，通过自动驾驶场景实验构建HEADD数据集，发现目的论解释质量显著优于反事实解释，为XAI设计提供了实证依据。
摘要：
人们常主张有效的以人为中心的可解释人工智能（XAI）应模仿人类推理。然而，关于认知科学概念如何辅助XAI设计的实证研究仍然缺乏。基于认知科学的见解，我们提出一个解释模式框架，用于分析人们如何构建解释框架，无论是机制性的、目的论的还是反事实的。利用自动驾驶这一复杂的安全关键领域，我们开展了一项包含两项研究的实验：（i）人们在14个独特场景中如何解释车辆行为（N1=54），以及（ii）他们如何感知这些解释（N2=382），并构建了新颖的自动驾驶决策人类解释（HEADD）数据集。我们的主要发现是，参与者认为目的论解释的质量显著优于反事实解释，且感知到的目的论是感知质量的最佳预测因子。基于这些结果，我们认为解释模式是设计和评估XAI时的重要分析维度，并强调需要以原则性和实证为基础来理解解释的认知机制。HEADD数据集和我们的代码可在以下地址获取：https://datashare.ed.ac.uk/handle/10283/8930。
英文摘要：
It is often argued that effective human-centered explainable artificial intelligence (XAI) should resemble human reasoning. However, empirical investigations of how concepts from cognitive science can aid the design of XAI are lacking. Based on insights from cognitive science, we propose a framework of explanatory modes to analyze how people frame explanations, whether mechanistic, teleological, or counterfactual. Using the complex safety-critical domain of autonomous driving, we conduct an experiment consisting of two studies on (i) how people explain the behavior of a vehicle in 14 unique scenarios (N1=54) and (ii) how they perceive these explanations (N2=382), curating the novel Human Explanations for Autonomous Driving Decisions (HEADD) dataset. Our main finding is that participants deem teleological explanations significantly better quality than counterfactual ones, with perceived teleology being the best predictor of perceived quality. Based on our results, we argue that explanatory modes are an important axis of analysis when designing and evaluating XAI and highlight the need for a principled and empirically grounded understanding of the cognitive mechanisms of explanation. The HEADD dataset and our code are available at: https://datashare.ed.ac.uk/handle/10283/8930.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
