國立高雄大學圖資館 |

Language: English

Back

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Explainable artificial intelligenceedited by Luca Longo, Sebastian Lapuschkin, Christin Seifert.
Reminder of title:	second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024 : proceedings.
remainder title:	xAI 2024
other author:	Longo, Luca.
corporate name:
Published:	Cham :Springer Nature Switzerland :2024.
Description:	xvii, 514 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence
Online resource:	https://doi.org/10.1007/978-3-031-63797-1
ISBN:	9783031637971$q(electronic bk.)

Explainable artificial intelligencesecond 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 ;229..

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$q(electronic bk.)

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

252958
Artificial intelligence

LC Class. No.: Q334

Dewey Class. No.: 006.3

Explainable artificial intelligencesecond World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024 : proceedings.Part II /
LDR:04247nmm a2200349 a 4500 001 666627
003 DE-He213
005 20240710130426.0
006 m d
007 cr nn 008maaau
008 250107s2024 sz s 0 eng d
020 $a 9783031637971$q(electronic bk.)
020 $a 9783031637964$q(paper)
024 7 $a 10.1007/978-3-031-63797-1 $2 doi
035 $a 978-3-031-63797-1
040 $a GP $c GP
041 0 $a eng
050 4 $a Q334
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
090 $a Q334 $b .W927 2024
111 2 $n (3rd : $d 1998 : $c Amsterdam, Netherlands) $3 194767
245 1 0 $a Explainable artificial intelligence $h [electronic resource] : $b second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024 : proceedings. $n Part II / $c edited by Luca Longo, Sebastian Lapuschkin, Christin Seifert.
246 3 $a xAI 2024
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2024.
300 $a xvii, 514 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Communications in computer and information science, $x 1865-0937 ; $v 2154
505 0 $a 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?.
520 $a 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.
650 0 $a Artificial intelligence $3 252958
650 1 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Natural Language Processing (NLP) $3 826373
650 2 4 $a Computer and Information Systems Applications. $3 913125
650 2 4 $a Computer Communication Networks. $3 218087
700 1 $a Longo, Luca. $3 787312
700 1 $a Lapuschkin, Sebastian. $3 979383
700 1 $a Seifert, Christin. $3 979384
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Communications in computer and information science ; $v 229. $3 547963
856 4 0 $u https://doi.org/10.1007/978-3-031-63797-1
950 $a Computer Science (SpringerNature-11645)