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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 / edited by Fadi Al Machot, Martin T. Horsch, Sebastian Scholze.

Springer eBooks EBA - Intelligent Technologies and Robotics Collection 2025 Available online

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Format:
Book
Contributor:
Al Machot, Fadi., Editor.
Horsch, Martin T., Editor.
Scholze, Sebastian., Editor.
Series:
Lecture Notes in Networks and Systems, 2367-3389 ; 1375
Language:
English
Subjects (All):
Computational intelligence.
Artificial intelligence.
Computational Intelligence.
Artificial Intelligence.
Local Subjects:
Computational Intelligence.
Artificial Intelligence.
Physical Description:
1 online resource (VI, 193 p. 23 illus., 16 illus. in color.)
Edition:
1st ed. 2025.
Place of Publication:
Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
Summary:
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.
Contents:
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.
ISBN:
3-031-89274-7
OCLC:
1524422513

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