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· 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.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
With $2.1 million funding from Google.org, the open-source Public Transit Intelligence Hub will unify public transit monitoring, operations, and passenger communication.
Able to defeat top-ranked human players and more efficient than other models, the new system could help decision-makers in military maneuvers or business negotiations.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.