Authors: Dr. Preeti Pandurang Kale, Dr. Subita Bhagat, Mrs. Drishya Darwin and Ms. S. Hemavathi

ISBN: 978-81-992568-5-9

DOI: https://doi.org/10.59646/eai/446

Date of Publication: September 10, 2025

About the Book:

As artificial intelligence (AI) systems increasingly influence critical decisions across healthcare, finance, law, autonomous systems, and daily consumer applications, the demand for transparency, interpretability, and trust has never been higher. While conventional “black-box” AI models often achieve high performance, their opacity raises ethical, legal, and societal concerns that cannot be ignored. This book explores the principles, methodologies, and practical strategies for building explainable AI (XAI) systems that balance performance with interpretability. It provides a comprehensive guide covering foundational concepts, post-hoc explanation techniques, inherently interpretable models, evaluation frameworks, ethical and legal considerations, and real-world applications. By integrating theory with practice, this text aims to empower AI practitioners, researchers, and decision-makers to design systems that are not only accurate but also transparent, fair, and accountable—ultimately fostering trust in AI-enabled technologies and their responsible deployment in society.