The rapidly evolving field of Explainable Artificial Intelligence (XAI) has generated significant interest in developing methods to make AI systems more transparent and understandable. However, the problem of explainability cannot be exhaustively solved in the abstract, as there is no single approach that can be universally applied to generate adequate explanations for any given AI system, and this is especially true in the arts. In this position paper, we propose an Explanatory Pragmatism (EP) framework for XAI in music performance, emphasising the importance of context and audience in the development of explainability requirements. By tailoring explanations to specific audiences and continuously refining them based on feedback, EP offers a promising direction for enhancing the transparency and interpretability of AI systems in broad artistic applications and more specifically to music performance.
Exploring Variational Auto-Encoder Architectures, Configurations, and Datasets for Generative Music Explainable AI
Creative Computing Institute, University of the Arts London（伦敦艺术大学创意计算学院）；School of Electronic Engineering and Computer Science, Queen Mary University of London（伦敦大学玛丽皇后学院电子工程与计算机科学学院）；Carleton College（卡尔顿学院）
LENS-XAI: Redefining Lightweight and Explainable Network Security through Knowledge Distillation and Variational Autoencoders for Scalable Intrusion Detection in Cybersecurity
Kırıkkale University（克尔克卡拉大学）；University College Dublin（都柏林大学学院）；CeADAR；School of Computer Science（计算机科学学院）
FedXDS: Leveraging Model Attribution Methods to counteract Data Heterogeneity in Federated Learning
Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；Technical University of Berlin（柏林工业大学）；Berlin Institute for the Foundations of Learning and Data (BIFOLD)（柏林学习与数据基础研究所）
Reduced NEXI protocol for the quantification of human gray matter microstructure on the Connectome 2.0 scanner
Lausanne University Hospital (CHUV) and University of Lausanne（拉沃斯大学医院（CHUV）和拉沃斯大学）；Massachusetts General Hospital（麻省总医院）；Athinoula A. Martinos Center for Biomedical Imaging（阿提诺拉A.马丁诺斯生物医学成像中心）
Explaining AI Without Code: A User Study on Explainable AI
Department of Computer Science, University of Chile（智利大学计算机科学系）；CENIA – National Center for Artificial Intelligence（人工智能国家研究中心）；Universidad Técnica Federico Santa María（弗朗西斯科·桑塔玛利亚技术大学）；IMFD - Millennium Institute for Foundational Research on Data（数据基础研究千年研究所）
Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation Test
BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所（BIFOLD））；Department of Electrical Engineering and Computer Science, TU Berlin（柏林工业大学电气工程与计算机科学系）；Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所人工智能系）；UMI Lab, Leibniz Institute of Agricultural Engineering and Bioeconomy e.V. (ATB)（莱布尼茨农业工程与生物经济研究所UMI实验室）；Department of Computer Science, University of Potsdam（波茨坦大学计算机科学系）
A three-Level Framework for LLM-Enhanced eXplainable AI: From technical explanations to natural language
Andalusian Research Institute in Data Science and Computational Intelligence（数据科学与计算智能安达卢西亚研究机构）；Universidad de Granada（格拉纳达大学）；Department of Computer Science（计算机科学系）；Artificial Intelligence Lab（人工智能实验室）；Vrije Universiteit Brussel（布鲁塞尔自由大学）
Choose Your Explanation: A Comparison of SHAP and GradCAM in Human Activity Recognition
Faculty of Informatics, Norwegian University of Science and Technology（1 信息学院，挪威科学与技术大学）；Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology（2 医学与健康科学学院，挪威科学与技术大学）；Department of Clinical and Molecular Medicine, NTNU（3 临床与分子医学系，NTNU）；Clinic of Rehabilitation, St. Olavs Hospital, Trondheim University Hospital（4 康复诊所，圣奥拉夫医院，特伦德尔姆大学医院）
DiCE-Extended: A Robust Approach to Counterfactual Explanations in Machine Learning
Faculty of Graduate Education Institute, Department of Artificial Intelligence (Interdisciplinary), Bahçeşehir University, Turkey（研究生教育学院人工智能系（跨学科）巴塞希尔大学，土耳其）；School of Computer Science, University College Dublin, Ireland（计算机科学学院都柏林大学学院，爱尔兰）；Faculty of Engineering and Natural Sciences, Department of Mathematics, Bahçeşehir University, Turkey（工程与自然科学学院数学系巴塞希尔大学，土耳其）
Input-Envelope-Output: Auditable Generative Music Rewards in Sensory-Sensitive Contexts
College of Science, Mathematics and Technology, Wenzhou-Kean University（温州肯恩大学理工商学院）；College of Science and Engineering, University of Minnesota（明尼苏达大学理工科学院）；School of Electrical and Computer Engineering, Georgia Institute of Technology（佐治亚理工学院电气与计算机工程学院）；School of Mathematics, Georgia Institute of Technology（佐治亚理工学院数学学院）
TU Berlin（柏林工业大学）；Fraunhofer HHI（弗劳恩霍夫海因里希·赫兹研究所）；Leibniz Institute of Agricultural Engineering and Bioeconomy e.V. (ATB)（莱布尼茨农业工程与生物经济研究所）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；University of Potsdam（波茨坦大学）
