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Record Nr. |
UNINA9910755085003321 |
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Autore |
Longo Luca |
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Titolo |
Explainable Artificial Intelligence : First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Part I / / edited by Luca Longo |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
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ISBN |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (711 pages) |
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Collana |
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Communications in Computer and Information Science, , 1865-0937 ; ; 1901 |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Natural language processing (Computer science) |
Application software |
Computer networks |
Artificial Intelligence |
Natural Language Processing (NLP) |
Computer and Information Systems Applications |
Computer Communication Networks |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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 |
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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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