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[null,"1.","Record Nr.",null,"UNINA9910872194103321"]
[null,null,"Autore",null,"Longo Luca"]
[null,null,"Titolo",null,"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"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"3-031-63797-6"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (0 pages)"]
[null,null,"Collana",null,"Communications in Computer and Information Science, , 1865-0937 ; ; 2154"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Natural language processing (Computer science)"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Natural Language Processing (NLP)"]
[{"$nulls":4},"Computer and Information Systems Applications"]
[{"$nulls":4},"Computer Communication Networks"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"-- XAI for graphs and Computer vision.  -- Model-Agnostic Knowledge Graph Embedding Explanations for Recommender Systems.  -- Graph-Based Interface for Explanations by Examples in Recommender Systems: A User Study.  -- Explainable AI for Mixed Data Clustering.  -- Explaining graph classifiers by unsupervised node relevance attribution.  -- Explaining Clustering of Ecological Momentary Assessment through Temporal and Feature-based Attention.  -- Graph Edits for Counterfactual Explanations: A comparative study.  -- Model guidance via explanations turns image classifiers into segmentation models.  -- Understanding the Dependence of Perception Model Competency on Regions in an Image.  -- A Guided Tour of Post-hoc XAI Techniques in Image Segmentation.  -- Explainable Emotion Decoding for Human and Computer Vision.  -- Explainable concept mappings of MRI: Revealing the mechanisms underlying deep learning-based brain disease classification.  -- Logic, reasoning, and rule-based explainable AI.  -- Template Decision Diagrams for Meta Control and Explainability.  -- A Logic of Weighted Reasons for Explainable Inference in AI.  -- On "]
[{"$nulls":4},"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?."]
[null,null,null,"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."]