Explainable Artificial Intelligence: Second World Conference, Xai 2024, Valletta, Malta, July 17-19, 2024, Proceedings, Part I (Paperback, 2024)

.- 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.