Title Cheruvalath, S. S., Laporte, M., Bombassei De Bona, F., Hassan, T., & Gjoreski, M. (2025, October). Generating Explanations for Models Predicting Student Exam Performance. In Companion of the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 1679-1684).
Abstract Understanding and improving student performance is a central concern in education, and predictive models can provide valuable insights—provided their decisions are transparent and explainable. However, many machine learning (ML) models used for this purpose lack interpretability, limiting their practical utility.
Title Vered, M., Hassan, T., Ntekouli, M., Bae, S. W., & Gjoreski, M. (2025, October). XAI for U 2025: 2 nd International Workshop on Explainable AI for Ubiquitous, Pervasive and Wearable Computing. In Companion of the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 1670-1673).
Abstract The workshop XAI for U aims to address the critical need for transparency in Artificial Intelligence (AI) systems that are increasingly integrated into our daily lives through mobile systems, wearables, and smart environments.
Title Buschmeier, H., Hassan, T., & Kopp, S. (2024, November). Multimodal Co-Construction of Explanations with XAI Workshop. In Proceedings of the 26th International Conference on Multimodal Interaction (pp. 698-699).
Abstract The ICMI 2024 workshop on “Multimodal Co-Construction of Explanations with XAI” bridges the fields of Explainable Artificial Intelligence (XAI) and Multimodal Interaction, focusing on the recent perspective that effective AI explanations should be dynamically co-constructed through interactive, social processes involving both the explainer and the explainee.
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.
Title Schneider, J., Cheruvalath, S. S., & Hassan, T. (2024, October). Time for an Explanation: A Mini-Review of Explainable Physio-Behavioural Time-Series Classification. In Companion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 885-889).
Abstract Time-series classification is seeing growing importance as device proliferation has lead to the collection of an abundance of sensor data. Although black-box models, whose internal workings are difficult to understand, are a common choice for this task, their use in safety-critical domains has raised calls for greater transparency.
