000 05620nam a22005895i 4500
001 978-3-031-63797-1
003 DE-He213
005 20240730172746.0
007 cr nn 008mamaa
008 240710s2024 sz | s |||| 0|eng d
020 _a9783031637971
_9978-3-031-63797-1
024 7 _a10.1007/978-3-031-63797-1
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 0 _aExplainable Artificial Intelligence
_h[electronic resource] :
_bSecond World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024, Proceedings, Part II /
_cedited by Luca Longo, Sebastian Lapuschkin, Christin Seifert.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXVII, 514 p. 159 illus., 140 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aCommunications in Computer and Information Science,
_x1865-0937 ;
_v2154
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 _aThis 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 _aArtificial intelligence.
_93407
650 0 _aNatural language processing (Computer science).
_94741
650 0 _aApplication software.
_9105359
650 0 _aComputer networks .
_931572
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aNatural Language Processing (NLP).
_931587
650 2 4 _aComputer and Information Systems Applications.
_9105362
650 2 4 _aComputer Communication Networks.
_9105363
700 1 _aLongo, Luca.
_eeditor.
_0(orcid)
_10000-0002-2718-5426
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9105364
700 1 _aLapuschkin, Sebastian.
_eeditor.
_0(orcid)
_10000-0002-0762-7258
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9105365
700 1 _aSeifert, Christin.
_eeditor.
_0(orcid)
_10000-0002-6776-3868
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9105367
710 2 _aSpringerLink (Online service)
_9105368
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031637964
776 0 8 _iPrinted edition:
_z9783031637988
830 0 _aCommunications in Computer and Information Science,
_x1865-0937 ;
_v2154
_9105370
856 4 0 _uhttps://doi.org/10.1007/978-3-031-63797-1
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cEBK
999 _c88536
_d88536