People Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior: Insights from Cognitive Science for Explainable AI

要旨

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 ($N_1=54$) and (ii) how they perceive these explanations ($N_2=382$), 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: \url{https://datashare.ed.ac.uk/handle/10283/8930}.

著者
Balint Gyevnar
University of Edinburgh, Edinburgh, United Kingdom
Stephanie Droop
University of Edinburgh, Edinburgh, United Kingdom
Tadeg Quillien
University of Edinburgh, Edinburgh, United Kingdom
Shay Cohen
University of Edinburgh, Edinburgh, United Kingdom
Neil R.. Bramley
University of Edinburgh, Edinburgh, Scotland, United Kingdom
Christopher Guy. Lucas
University of Edinburgh, Edinburgh, United Kingdom
Stefano V.. Albrecht
University of Edinburgh, Edinburgh, United Kingdom
DOI

10.1145/3706598.3713509

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713509

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Autonomus Vehicle

Annex Hall F204
7 件の発表
2025-05-01 18:00:00
2025-05-01 19:30:00
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