This comprehensive book on Explainable Artificial Intelligence has been updated and expanded to reflect the latest developments in the field of XAI, enriching the existing literature with new research, case studies, and practical techniques. The Second Edition expands upon its predecessor by addressing developments in AI, including large language models and multi-modal systems that incorporate text, visual, audio, and sensory data. The need to make complex systemic structures understandable without sacrificing performance is emphasized, providing a reinforced focus on additive models for improved explainability. Balancing technical rigor with accessibility, the book combines theory with practical application, equipping readers with the skills needed to effectively implement explainable AI (XAI) methods in real-world environments. Features:

• Extension of the "Internally Explainable Models" chapter to delve into generalized additive models and other intrinsic techniques, enriching the chapter with new examples and use cases for better understanding of intrinsic XAI models.
• Additional details in "Model-Agnostic Methods for XAI" focusing on how explanations differ between training and testing datasets, including a new model to better illustrate these differences more clearly and effectively.
• New section in "Making Science with Machine Learning and XAI" presenting a visual approach to learning the fundamentals of XAI, making the concept more accessible to readers through an interactive and engaging interface.
• Revisions in "Reverse Engineering and Explainability" including code reviews to enhance understanding and effectiveness of discussed concepts, ensuring code examples are up-to-date and optimized for current best practices.
• New chapter "Genetic Models and Large Language Models (LLM)" dedicated to genetic models and large language models, exploring their role in XAI and how they can be used to create richer, more interactive explanations. This chapter also covers the explainability of transformer models and privacy protection through genetic models.
• New "Mini-Chapter on Artificial General Intelligence and XAI" exploring the implications of Artificial General Intelligence (AGI) for XAI, discussing how advances toward AGI systems influence strategies and methodologies for XAI.
• Improvements to "Explaining Deep Learning Models" with new methodologies in explaining deep learning models, further enriched with innovative techniques and insights for deeper understanding.

Pages: 324, Year of Publication: 0806, Dimensions: 15.5x15.5cm