Explainable Artificial Intelligence : Second World Conference, xAI 2024, Valletta, Malta, July 17–19, 2024, Proceedings, Part II / / edited by Luca Longo, Sebastian Lapuschkin, Christin Seifert

• Autore: Longo Luca
• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
• Edizione: 1st ed. 2024.
• Descrizione fisica: 1 online resource (0 pages)
• Disciplina: 006.3
• Soggetto topico: Artificial intelligence; Natural language processing (Computer science); Application software; Computer networks; Artificial Intelligence; Natural Language Processing (NLP); Computer and Information Systems Applications; Computer Communication Networks
• Altri autori: LapuschkinSebastian SeifertChristin
• Nota di contenuto: -- 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?.
• Sommario/riassunto: This four-volume set constitutes the refereed proceedings of the Second World Conference on Explainable Artificial Intelligence, xAI 2024, held in Valletta, Malta, during July 17-19, 2024. The 95 full papers presented were carefully reviewed and selected from 204 submissions. The conference papers are organized in topical sections on: Part I - intrinsically interpretable XAI and concept-based global explainability; generative explainable AI and verifiability; notion, metrics, evaluation and benchmarking for XAI. Part II - XAI for graphs and computer vision; logic, reasoning, and rule-based explainable AI; model-agnostic and statistical methods for eXplainable AI. Part III - counterfactual explanations and causality for eXplainable AI; fairness, trust, privacy, security, accountability and actionability in eXplainable AI. Part IV - explainable AI in healthcare and computational neuroscience; explainable AI for improved human-computer interaction and software engineering for explainability; applications of explainable artificial intelligence.
• Titolo autorizzato: Explainable Artificial Intelligence
• ISBN: 3-031-63797-6
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910872194103321
• Lo trovi qui: Univ. Federico II
• Serie: Communications in Computer and Information Science, . 1865-0937 ; ; 2154