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. This item is unavailable.
Language: English
Published by Springer, 2025
- Softcover
- New
Condition: New
US$ 172.32
Item description from seller
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.
Seller Inventory # 133208948
- Title
- 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
- Author
- Fadi Al Machot (u. a.)
- Publisher
- Springer
- Publication year
- 2025
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 3031892739
- ISBN 13
- 9783031892738
- Item weight
- 312 grams
- Dimensions
- 235 x 155 x 12 mm
- Seller catalogs
- Bücher
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.