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Explainable artificial intelligence = second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024 : proceedings.. Part II /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Explainable artificial intelligence/ edited by Luca Longo, Sebastian Lapuschkin, Christin Seifert.
其他題名:	second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024 : proceedings.
其他題名:	xAI 2024
其他作者:	Longo, Luca.
團體作者:	World Conference on Explainable Artificial Intelligence
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	xvii, 514 p. :ill. (some col.), digital ;24 cm.
內容註:	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?.
Contained By:	Springer Nature eBook
標題:	Artificial intelligence - Congresses. -
電子資源:	https://doi.org/10.1007/978-3-031-63797-1
ISBN:	9783031637971

Explainable artificial intelligence = second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024 : proceedings.. Part II /
Explainable artificial intelligencesecond World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024 : proceedings.Part II /[electronic resource] :xAI 2024edited by Luca Longo, Sebastian Lapuschkin, Christin Seifert. - Cham :Springer Nature Switzerland :2024. - xvii, 514 p. :ill. (some col.), digital ;24 cm. - Communications in computer and information science,21541865-0937 ;. - Communications in computer and information science ;2154..

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?.

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.

ISBN: 9783031637971

Standard No.: 10.1007/978-3-031-63797-1doiSubjects--Topical Terms:

606815
Artificial intelligence
--Congresses.

LC Class. No.: Q334

Dewey Class. No.: 006.3

Explainable artificial intelligence = second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024 : proceedings.. Part II /
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