.- XAI for graphs and Computer vision. .- Model-Agnostic Knowledge Graph Embedding Explanations for Recommender Systems. .- Graph-Based Interface for Explanations by Examples in Recommender Systems: A User Study. .- Explainable AI for Mixed Data Clustering. .- Explaining graph classifiers by unsupervised node relevance attribution. .- Explaining Clustering of Ecological Momentary Assessment through Temporal and Feature-based Attention. .- Graph Edits for Counterfactual Explanations: A comparative study. .- Model guidance via explanations turns image classifiers into segmentation models. .- Understanding the Dependence of Perception Model Competency on Regions in an Image. .- A Guided Tour of Post-hoc XAI Techniques in Image Segmentation. .- Explainable Emotion Decoding for Human and Computer Vision. .- Explainable concept mappings of MRI: Revealing the mechanisms underlying deep learning-based brain disease classification. .- Logic, reasoning, and rule-based explainable AI. .- Template Decision Diagrams for Meta Control and Explainability. .- A Logic of Weighted Reasons for Explainable Inference in AI. .- On Explaining and Reasoning about Fiber Optical Link Problems. .- Construction of artificial most representative trees by minimizing tree-based distance measures. .- Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles. .- Model-agnostic and statistical methods for eXplainable AI. .- Observation-specific explanations through scattered data approximation. .- CNN-based explanation ensembling for dataset, representation and explanations evaluation. .- Local List-wise Explanations of LambdaMART. .- Sparseness-Optimized Feature Importance. .- Stabilizing Estimates of Shapley Values with Control Variates. .- A Guide to Feature Importance Methods for Scientific Inference. .- Interpretable Machine Learning for TabPFN. .- Statistics and explainability: a fruitful alliance. .- How Much Can Stratification Improve the Approximation of Shapley Values?.