What Do People Want to Know about Artificial Intelligence (AI)? The Importance of Answering End-user Questions to Explain Autonomous Vehicle (AV) Decisions

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.

Authors

Institutions

Publication Details

Journal
Proceedings of the ACM on Human-Computer Interaction
Published
2025-10-16
DOI
https://doi.org/10.1145/3757437
Primary Topic
Explainable Artificial Intelligence (XAI)
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

What Do People Want to Know about Artificial Intelligence (AI)? The Importance of Answering End-user Questions to Explain Autonomous Vehicle (AV) Decisions

Nikola Banović, Somayeh Molaei, Lionel Robert
Proceedings of the ACM on Human-Computer Interaction
Explainable Artificial Intelligence (XAI)
article

What Do People Want to Know about Artificial Intelligence (AI)? The Importance of Answering End-user Questions to Explain Autonomous Vehicle (AV) Decisions

Nikola Banović, Somayeh Molaei, Lionel Robert
article en

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.

Proceedings of the ACM on Human-Computer InteractionVol. 9(7)
University of Michigan (US)
Openalex Percentile: Top 86%
Explainable Artificial Intelligence (XAI)
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

What Do People Want to Know about Artificial Intelligence (AI)? The Importance of Answering End-user Questions to Explain Autonomous Vehicle (AV) Decisions — Nikola Banović, Somayeh Molaei, et al. · Proceedings of the ACM on Human-Computer Interaction (2025) | TGRS Research Map | TGRS