This book focuses on explainable-AI-ready (XAIR) data and models, offering a comprehensive perspective on the foundations needed for transparency, interpretability, and trust in AI systems. It introduces novel strategies for metadata structuring, conceptual analysis, and validation frameworks, addressing critical challenges in regulation, ethics, and responsible machine learning.
Furthermore, it highlights the importance of standardized documentation and conceptual clarity in AI validation, ensuring that systems remain transparent and accountable.
Aimed at researchers, industry professionals, and policymakers, this resource provides insights into AI governance and reliability. By integrating perspectives from applied ontology, epistemology, and AI assessment, it establishes a structured framework for developing robust, trustworthy, and explainable AI technologies.
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book focuses on explainable-AI-ready (XAIR) data and models, offering a comprehensive perspective on the foundations needed for transparency, interpretability, and trust in AI systems. It introduces novel strategies for metadata structuring, conceptual analysis, and validation frameworks, addressing critical challenges in regulation, ethics, and responsible machine learning.Furthermore, it highlights the importance of standardized documentation and conceptual clarity in AI validation, ensuring that systems remain transparent and accountable.Aimed at researchers, industry professionals, and policymakers, this resource provides insights into AI governance and reliability. By integrating perspectives from applied ontology, epistemology, and AI assessment, it establishes a structured framework for developing robust, trustworthy, and explainable AI technologies. 193 pp. Englisch. N° de réf. du vendeur 9783031892738
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Taschenbuch. Etat : Neu. Designing the Conceptual Landscape for a XAIR Validation Infrastructure | Proceedings of the International Workshop on Designing the Conceptual Landscape for a XAIR Validation Infrastructure, DCLXVI 2024, Kaiserslautern, Germany | Fadi Al Machot (u. a.) | Taschenbuch | Lecture Notes in Networks and Systems | vi | Englisch | 2025 | Springer | EAN 9783031892738 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. N° de réf. du vendeur 133208948
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book focuses on explainable-AI-ready (XAIR) data and models, offering a comprehensive perspective on the foundations needed for transparency, interpretability, and trust in AI systems. It introduces novel strategies for metadata structuring, conceptual analysis, and validation frameworks, addressing critical challenges in regulation, ethics, and responsible machine learning.Furthermore, it highlights the importance of standardized documentation and conceptual clarity in AI validation, ensuring that systems remain transparent and accountable.Aimed at researchers, industry professionals, and policymakers, this resource provides insights into AI governance and reliability. By integrating perspectives from applied ontology, epistemology, and AI assessment, it establishes a structured framework for developing robust, trustworthy, and explainable AI technologies.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 200 pp. Englisch. N° de réf. du vendeur 9783031892738
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