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Explainable Artificial Intelligence edited by Luca Longo. [electronic resource] :

Contributor(s): Material type: TextTextPublication details: Cham Springer Nature Switzerland 2023Edition: 1st ed. 2023Description: XVIII, 664 p. 193 illus., 176 illus. in color. online resourceISBN:
  • 9783031440670
Subject(s): DDC classification:
  • 6.3
Online resources: Summary: This three-volume set constitutes the refereed proceedings of the First World Conference on Explainable Artificial Intelligence, xAI 2023, held in Lisbon, Portugal, in July 2023. The 94 papers presented were thoroughly reviewed and selected from the 220 qualified submissions. They are organized in the following topical sections: Part I: Interdisciplinary perspectives, approaches and strategies for xAI; Model-agnostic explanations, methods and techniques for xAI, Causality and Explainable AI; Explainable AI in Finance, cybersecurity, health-care and biomedicine. Part II: Surveys, benchmarks, visual representations and applications for xAI; xAI for decision-making and human-AI collaboration, for Machine Learning on Graphs with Ontologies and Graph Neural Networks; Actionable eXplainable AI, Semantics and explainability, and Explanations for Advice-Giving Systems. Part III: xAI for time series and Natural Language Processing; Human-centered explanations and xAI for Trustworthy and Responsible AI; Explainable and Interpretable AI with Argumentation, Representational Learning and concept extraction for xAI.
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Item type Home library Call number Status Notes Date due Barcode Item holds
e-Book e-Book S. R. Ranganathan Learning Hub Online Available Platform:Springer EB2802
Total holds: 0

This three-volume set constitutes the refereed proceedings of the First World Conference on Explainable Artificial Intelligence, xAI 2023, held in Lisbon, Portugal, in July 2023. The 94 papers presented were thoroughly reviewed and selected from the 220 qualified submissions. They are organized in the following topical sections: Part I: Interdisciplinary perspectives, approaches and strategies for xAI; Model-agnostic explanations, methods and techniques for xAI, Causality and Explainable AI; Explainable AI in Finance, cybersecurity, health-care and biomedicine. Part II: Surveys, benchmarks, visual representations and applications for xAI; xAI for decision-making and human-AI collaboration, for Machine Learning on Graphs with Ontologies and Graph Neural Networks; Actionable eXplainable AI, Semantics and explainability, and Explanations for Advice-Giving Systems. Part III: xAI for time series and Natural Language Processing; Human-centered explanations and xAI for Trustworthy and Responsible AI; Explainable and Interpretable AI with Argumentation, Representational Learning and concept extraction for xAI.

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