.- Intrinsically interpretable XAI and concept-based global explainability. .- Seeking Interpretability and Explainability in Binary Activated Neural Networks. .- Prototype-based Interpretable Breast Cancer Prediction Models: Analysis and Challenges. .- Evaluating the Explainability of Attributes and Prototypes for a Medical Classification Model. .- Revisiting FunnyBirds evaluation framework for prototypical parts networks. .- CoProNN: Concept-based Prototypical Nearest Neighbors for Explaining Vision Models. .- Unveiling the Anatomy of Adversarial Attacks: Concept-based XAI Dissection of CNNs. .- AutoCL: AutoML for Concept Learning. .- Locally Testing Model Detections for Semantic Global Concepts. .- Knowledge graphs for empirical concept retrieval. .- Global Concept Explanations for Graphs by Contrastive Learning. .- Generative explainable AI and verifiability. .- Augmenting XAI with LLMs: A Case Study in Banking Marketing Recommendation. .- Generative Inpainting for Shapley-Value-Based Anomaly Explanation. .- Challenges and Opportunities in Text Generation Explainability. .- NoNE Found: Explaining the Output of Sequence-to-Sequence Models when No Named Entity is Recognized. .- Notion, metrics, evaluation and benchmarking for XAI. .- Benchmarking Trust: A Metric for Trustworthy Machine Learning. .- Beyond the Veil of Similarity: Quantifying Semantic Continuity in Explainable AI. .- Conditional Calibrated Explanations: Finding a Path between Bias and Uncertainty. .- Meta-evaluating stability measures: MAX-Sensitivity & AVG-Senstivity. .- Xpression: A unifying metric to evaluate Explainability and Compression of AI models. .- Evaluating Neighbor Explainability for Graph Neural Networks. .- A Fresh Look at Sanity Checks for Saliency Maps. .- Explainability, Quantified: Benchmarking XAI techniques. .- BEExAI: Benchmark to Evaluate Explainable AI. .- Associative Interpretability of Hidden Semantics with Contrastiveness Operators in Face Classification tasks.