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Explainable Artificial Intelligence

Second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024, Proceedings, Part II

Medium: Buch
ISBN: 978-3-031-63796-4
Verlag: Springer Nature Switzerland
Erscheinungstermin: 10.07.2024
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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.


Produkteigenschaften


  • Artikelnummer: 9783031637964
  • Medium: Buch
  • ISBN: 978-3-031-63796-4
  • Verlag: Springer Nature Switzerland
  • Erscheinungstermin: 10.07.2024
  • Sprache(n): Englisch
  • Auflage: 2024
  • Serie: Communications in Computer and Information Science
  • Produktform: Kartoniert
  • Gewicht: 797 g
  • Seiten: 514
  • Format (B x H x T): 155 x 235 x 29 mm
  • Ausgabetyp: Kein, Unbekannt
Autoren/Hrsg.

Herausgeber

.- 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?.