eXplainable AI

This book is intended for undergraduate (B.Tech) and postgraduate (M.Tech) students in Computer Science, Artificial Intelligence, Data Science, and related engineering disciplines. It is also a valuable resource for researchers, practitioners, policymakers, and anyone keen on understanding how we can build more responsible and human-centric AI systems.
Key Features of this Book:
1. NEP 2020 and AICTE Aligned: Content and pedagogy are harmonized with the latest Indian educational frameworks, promoting holistic and skill-based learning.
2. Comprehensive Coverage: Offers a panoramic view of XAI, from foundational theories and core techniques to advanced concepts and future research directions.
3. Practical Approach: Emphasizes understanding the "how" and "why" behind XAI methods, with discussions on implementation considerations, tools, and real-world case studies to bridge theory and practice.
4. Cutting-Edge Content: Incorporates the latest advancements and research in XAI, including techniques for deep learning models, counterfactuals, and concept-based explanations.
5. Focus on Ethics and Responsible AI: Integrates dedicated discussions on fairness, bias mitigation, accountability, and the societal impact of XAI, fostering a responsible mindset.
6. Global Perspective: While rooted in Indian educational standards, the content is curated to be relevant and valuable to students and professionals worldwide.
7. Structured Learning Path: The ten-chapter structure provides a logical progression, building knowledge incrementally from basic concepts to more complex topics.
8. Clarity and Accessibility: Complex topics are explained in a clear, concise, and accessible manner, supported by diagrams, examples, and intuitive explanations to aid understanding.
9. Updated and Relevant: Content reflects the current state-of-the-art in XAI, ensuring readers are equipped with contemporary knowledge.
10. Skill Development: Aims to equip readers with the analytical skills to select, apply, and critically evaluate XAI methods in various contexts.
A unique aspect of this book is its emphasis on not just what these techniques are, but how they work, when to use them, and what their limitations are. We will explore advanced concepts such as counterfactual explanations, concept-based explanations, and the crucial process of evaluating the quality and effectiveness of explanations. Recognizing that XAI is not an isolated discipline, we will dedicate significant attention to its practical applications across diverse domains like healthcare, finance, and autonomous systems, illustrating its real-world impact through pertinent case studies.

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