Gjoreski, M., et al. (2024). XAI for U: Explainable AI for Ubiquitous, Pervasive and Wearable Computing.
Title
Gjoreski, M., Hassan, T., Vered, M., Houben, S., & Kopp, S. (2024, October). XAI for U: Explainable AI for Ubiquitous, Pervasive and Wearable Computing. In Companion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 992-995).
Abstract
The workshop XAI for U aims to address the critical need for transparency in Artificial Intelligence (AI) systems that integrate into our daily lives through mobile systems, wearables, and smart environments. Despite advances in AI, many of these systems remain opaque, making it difficult for users, developers, and stakeholders to verify their reliability and correctness. This workshop addresses the pressing need for enabling Explainable AI (XAI) tools within Ubiquitous and Wearable Computing and highlights the unique challenges that come with it, such as XAI that deals with time-series and multimodal data, XAI that explains interconnected machine learning (ML) components, and XAI that provides user-centered explanations. The workshop aims to foster collaboration among researchers in related domains, share recent advancements, address open challenges, and propose future research directions to improve the applicability and development of XAI in Ubiquitous Pervasive and Wearable Computing - and with that seeks to enhance user trust, understanding, interaction, and adoption, ensuring that AI- driven solutions are not only more explainable but also more aligned with ethical standards and user expectations.