What Do People Want to Know about Artificial Intelligence (AI)? The Importance of Answering End-user Questions to Explain Autonomous Vehicle (AV) Decisions
Somayeh Molaei, Lionel Peter Robert, Nikola Banovic
Abstract
Improving end-users' understanding of decisions made by autonomous vehicles (AVs) driven by artificial intelligence (AI) can improve utilization and acceptance of AVs. However, current explanation mechanisms primarily help AI researchers and engineers in debugging and monitoring their AI systems, and may not address the specific questions of end-users, such as passengers, about AVs in various scenarios. In this paper, we conducted two user studies to investigate questions that potential AV passengers might pose while riding in an AV and evaluate how well answers to those questions improve their understanding of AI-driven AV decisions. Our initial formative study identified a range of questions about AI in autonomous driving that existing explanation mechanisms do not readily address. Our second study demonstrated that interactive text-based explanations effectively improved participants' comprehension of AV decisions compared to simply observing AV decisions. These findings inform the design of interactions that motivate end-users to engage with and inquire about the reasoning behind AI-driven AV decisions.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext a72ade6f-befd-494b-928f-e817953a8b0dBuilds on24
- What is AI Literacy? Competencies and Design ConsiderationsDuri Long, Brian MagerkoCHI 2020 · 2,947 citations
- To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-makingZana Buçinca, Maja Barbara Malaya, Krzysztof Z. GajosCSCW 2021 · 962 citations
- An Aligned Rank Transform Procedure for Multifactor Contrast TestsLisa A. Elkin, Matthew Kay, James J. Higgins, Jacob O. WobbrockUIST 2021 · 671 citations
- Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine LearningHarmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana et al.CHI 2020 · 541 citations
- Expanding Explainability: Towards Social Transparency in AI systemsUpol Ehsan, Q. Vera Liao, Michael J. Muller, Mark O. Riedl et al.CHI 2021 · 505 citations
Related papers
- When to Explain: Modeling User Need for Explanations in Real-World Autonomous DrivingShihong Ling, Yaohan Ding, Yu Liu, Yue Wan et al.CHI 2026 · 1 citation
- What and When to Explain?: On-road Evaluation of Explanations in Highly Automated VehiclesGwangbin Kim, Dohyeon Yeo, Taewoo Jo, Daniela Rus et al.UbiComp 2023 · 34 citations
- ExplAIn Yourself! Transparency for Positive UX in Autonomous DrivingTobias Schneider, Joana Hois, Alischa Rosenstein, Sabiha Ghellal et al.CHI 2021 · 74 citations
- What Did My Car Say? Impact of Autonomous Vehicle Explanation Errors and Driving Context On Comfort, Reliance, Satisfaction, and Driving ConfidenceRobert A. Kaufman, Aaron Broukhim, David Kirsh, Nadir WeibelCHI 2025 · 12 citations
- Decoding Driver Intention Cues: Exploring Non-verbal Communication for Human-Centered Automotive InterfacesMohammad Faramarzian, Jorge Pardo, Ilan Mandel, Andry Rakotonirainy et al.CHI 2025 · 3 citations
