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, ensu