XAI for U: Explainable AI for Ubiquitous, Pervasive and Wearable Computing
In conjunction with UbiComp'24 Melbourne, Australia
The XAI for U workshop is dedicated to addressing the critical need for transparency in AI systems embedded in our daily lives through mobile systems, wearables, and smart environments. The workshop aims to foster the development and application of Explainable AI (XAI) tools to overcome the opacity of these systems, focusing on the unique challenges of XAI in time-series and multimodal data, interconnected ML components, and user-centered explanations. This workshop offers a vital platform for sharing recent advancements, addressing open challenges, and proposing future research directions to ensure AI-driven solutions are explainable, ethical, and aligned with user expectations.
Call For Papers
We invite submissions of original research, insightful case studies, and work in progress that address XAI applications within Ubiquitous and Wearable Computing, including but not limited to:
XAI in time-series and multimodal data analysis
Techniques and challenges in interpreting complex data streams from wearable and ubiquitous computing devices.
User-centered explanations for AI-driven systems
Designing explanations that are meaningful and accessible to end-users.
Deployment and evaluation of XAI tools in real-world scenarios
Case studies and empirical research on the effectiveness of XAI applications.
Multimodal XAI for behavior analysis
Leveraging diverse data sources for comprehensive behavior analysis.
Interconnected ML components in wearable and ubiquitous computing
Strategies for explaining the dynamics and decisions of interconnected AI systems and models.
Ethical considerations and user privacy in XAI
Addressing the ethical implications and privacy concerns of deploying XAI in ubiquitous computing.
Multimodal XAI in affective computing
Techniques for understanding and interpreting human emotions through AI.
Empirical evaluation methods
Methods for assessing the effectiveness and impact of XAI and multimodal AI systems.
Paper format
Submissions should be anonymized and up to 4 pages (including references). ACM requires UbiComp/ISWC 2024 workshop submissions to use the double-column template. Please check the UbiComp website for more details about the template.
For camera-ready submission instructions please visit: https://www.scomminc.com/pp/acmsig/ubicomp.htm
Important Dates (AoE)
Submission deadline: June 7, 2024 June 28, 2024
Notification of acceptance: June 28, 2024 July 12, 2024
Camera-ready deadline: July 26, 2024
Workshop date: October 5-6, 2024
Registration
Conference registration now open!
Register at http://new.precisionconference.com/sigchi.
Registration code: UBC24-W11
Tentative Schedule
| Time | Event |
|---|---|
| 9:00 - 9:15 | Welcome |
| 9:15 - 10:00 | Keynote 1 + Q&A |
| 10:00 - 10:15 | Coffee break + Poster tour |
| 10:15 - 11:00 | Oral presentations – Part 1 |
| 11:00 - 11:45 | Keynote 2 + Q&A |
| 11:45 - 13:15 | Lunch break |
| 13:15 - 14:00 | Oral presentations – Part 2 |
| 14:00 - 15:00 | Panel discussion |
| 15:00 - 15:15 | Coffee break + Poster tour |
| 15:15 - 16:15 | Group discussion (3-4 groups) |
| 16:15 - 17:00 | Group discussion – summary |
| 17:00 - 17:30 | Conclusion and farewell |
Keynote Speaker
Accepted Papers
| Paper | Author |
|---|---|
| A Non-collocated Wearable Framework for Back Support Exoskeleton Payload Estimation (link) | Tian Lyu, Yiang Yu, Ashwin Narayan, Haoyong Yu |
| FairAD-XAI: Evaluation Framework for Explainable AI Methods in Alzheimer's Disease Detection with Fairness-in-the-loop (link) | Quoc-Toan Nguyen, Linh Le, Xuan-The Tran, Thomas Do, Chin-Teng Lin |
| How to Validate XAI in Longitudinal Studies? (link) | Martin Gjoreski, Matias Laporte, Marc Langheinrich, Tim Miller |
| Know Your Users: Towards Explainable AI in Bangladesh (link) | Farzana Islam, Tasmiah Tahsin Mayeesha, Nova Ahmed |
| Smartphone-based Human Behavior Task Modeling for Explainable Mental Health Detection Model (link) | Hansoo Lee, Taehyun Park, Uichin Lee |
| Time for an Explanation: A Mini-Review of Explainable Physio-Behavioural Time-Series Classification (link) | Jordan Schneider, Swathy Satheesan Cheruvalath, Teena Hassan |
| Towards Understanding Human-AI Reliance Patterns Through Explanation Styles (link) | Emma R. Casolin, Flora D. Salim |
| Using Large Language Models to Compare Explainable Models for Smart Home Human Activity Recognition (link) | Michele Fiori, Gabriele Civitarese, Claudio Bettini |
