Explainable Artificial Intelligence: Methods for Trustworthy AI Systems

Explainable Artificial Intelligence: Methods for Trustworthy AI Systems provides acomprehensive exploration of how artificial intelligence can become more transparent,interpretable, fair, and trustworthy. The book introduces the foundations and evolution ofExplainable AI (XAI), highlighting the growing need for transparency, accountability, andhuman trust in intelligent systems. It examines key explainability techniques, includingintrinsically interpretable models, feature attribution, model-agnostic methods, and local andglobal explanations. Special attention is given to deep learning, transformers, large languagemodels, generative AI, and foundation models. The book also addresses fairness, ethics, privacy,governance, and regulatory compliance, alongside practical tools, frameworks, evaluationmetrics, and human-centered assessment approaches. Through applications in healthcare,finance, cybersecurity, insurance, and critical infrastructure, it demonstrates the practical value oftrustworthy AI. The book concludes by examining emerging trends and future researchdirections, making it a valuable resource for researchers, professionals, educators, and studentsexploring responsible and explainable AI.