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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 /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Designing the conceptual landscape for a XAIR validation infrastructure/ edited by Fadi Al Machot, Martin T. Horsch, Sebastian Scholze.
其他題名:	proceedings of the International Workshop on Designing the Conceptual Landscape for a XAIR Validation Infrastructure, DCLXVI 2024, Kaiserslautern, Germany /
其他題名:	DCLXVI 2024
其他作者:	Al Machot, Fadi.
團體作者:	Workshop on the Preservation of Stability under Discretization
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	vi, 193 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Artificial intelligence -
電子資源:	https://doi.org/10.1007/978-3-031-89274-5
ISBN:	9783031892745

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 /
Designing the conceptual landscape for a XAIR validation infrastructureproceedings of the International Workshop on Designing the Conceptual Landscape for a XAIR Validation Infrastructure, DCLXVI 2024, Kaiserslautern, Germany /[electronic resource] :DCLXVI 2024edited by Fadi Al Machot, Martin T. Horsch, Sebastian Scholze. - Cham :Springer Nature Switzerland :2025. - vi, 193 p. :ill. (some col.), digital ;24 cm. - Lecture notes in networks and systems,13752367-3389 ;. - Lecture notes in networks and systems ;v.1..

Synopsis of core concepts for explainable AI-ready data and models -- Conceptualizing validation systems for explainable AI A design approach -- Balancing performance and transparency -- Explainable AI for battery health monitoring -- A minimalistic definition of XAI explanations -- A comparative analysis of deep learning architectures and explainable AI -- Conclusion.

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.

ISBN: 9783031892745

Standard No.: 10.1007/978-3-031-89274-5doiSubjects--Topical Terms:

671585
Artificial intelligence

LC Class. No.: Q334

Dewey Class. No.: 006.3

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 /
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