Designing the Conceptual Landscape for a XAIR Validation Infrastructure

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.

Presents a survey the conceptual landscape of explainable-AI-ready (XAIR) models and data Investigates the concepts for developing and documenting AI systems that are transparent, interpretable, and trustworthy Explores the potential of metadata standardization as well as conceptual analysis and engineering for AI validation

Inhalt

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.