2nd Workshop on Exploring the Potential of XAI and HMI to Alleviate Ethical, Legal, and Social Conflicts in Automated Vehicles: Applying HCXAI to Discover User Explainability Needs
Sep 2026· Adjunct Proceedings of the 18th International Conference on Automotive User Interfaces and Interactive Vehicular Applications· pp. 350-353· 0 citations· 5 references
Abstract
As automated vehicles (AVs) are increasingly deployed in real-world environments, users often do not understand what decisions their vehicles are making, or why. Explainable AI (XAI) offers a promising path toward transparency, yet most existing approaches remain algorithm-centric and inaccessible to end users. This workshop explores how Human-Centered XAI (HCXAI) and the Question-Driven Design framework can be used to systematically discover user explainability needs for AVs. Through scenario-based, participatory activities, participants will adopt different stakeholder perspectives and generate the questions they would want an AV to answer in ambiguous driving situations. Building on the prior workshop at AutomotiveUI 2025, this workshop aims to produce an initial taxonomy of AV-specific explanation needs grounded in real user questions, creating a shared resource that can guide future research, UI design, and evaluation of explainable automated driving systems.
WSM-Aware HRI is proposed, an IoT-enhanced modular framework that unifies diverse HRI breakdowns as World-State Mismatches (WSMs) between a human's instruction-implied assumptions and a robot's grounded world model built from multimodal perception and digital augmentation.
Han-Lin Zhang, Yu-Quan Wang, Tian-Wei Zhang et al.· 0 citations
A persona-driven, multi-agent workflow in which User-Agents execute Perceive–Decide–Act loops on rendered prototypes while a Supervisor-Agent verifies trace integrity and maps evidenced failures to established standards (ISO 9241-110 and Nielsen’s heuristics).
Oussama Touir, Farah Barika Ktata, M. Soui· 0 citations
Computer-use agents (CUAs), while capable of completing computer tasks in everyday and professional workflows, can cause unintended harm even under benign instructions and environments. However, detecting such harm remains challenging. First, it requires careful, task-specific reasoning: verifiers guided only by genera...
Jian-Xing Chen, Xiao Yu, Shipra Agrawal et al.· 0 citations
Artificial intelligence (AI) systems are increasingly central to decisions that impact individuals and society, showing the need for these systems to align with stakeholder values. However, traditional engineering approaches often relegate values to implicit considerations or early design phases, which diminishes their...
UI-Venus-2 is presented, a general-purpose foundation GUI agent designed to operate across mobile, web, and desktop environments through a unified closed-loop reasoning-action framework that integrates safety-aware mechanisms to ensure controlled execution of consequential actions.
Venus Team, Zhuo-Hang Cai, Hao-Xin Chen et al.· 1 citation
Explanations in automated vehicles have been proposed to support users’ understanding, trust, and acceptance of automated driving behavior. However, many explanation interfaces remain static interventions, even though users’ information needs may vary depending on driving context, familiarity, perceived criticality, an...
Philipp Asteriou, Ambika Shahu, Philipp Wintersberger· Adjunct Proceedings of the 1...· 0 citations
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