Principles of Explainable Artificial Intelligence

Advanced Interpretability, Trustworthy AI & Large Language Model Explainability (Vol. 2)

As Artificial Intelligence becomes increasingly integrated into healthcare, finance, autonomous vehicles, cybersecurity, education, and public decision-making, the demand for transparent, interpretable, fair, and trustworthy AI systems has never been greater. Modern AI models, particularly deep neural networks and Large Language Models (LLMs), deliver remarkable performance but often operate as highly complex black-box systems.

Principles of Explainable Artificial Intelligence: Advanced Interpretability, Trustworthy AI & Large Language Model Explainability (Vol. 2) builds upon the mathematical foundations introduced in Volume 1 by presenting advanced methods for interpreting, validating, evaluating, and verifying modern AI systems.

The book provides comprehensive coverage of LIME, Integrated Gradients, DeepLIFT, Anchors, TCAV, SHAP extensions, interpretable machine learning, deep neural network explainability, Grad-CAM, Layer-wise Relevance Propagation, transformer interpretability, attention analysis, attribution in Large Language Models, fairness metrics, AI ethics, formal verification, model checking, and future research directions in Explainable AI.

Readers will gain both theoretical understanding and practical insight into designing transparent AI systems capable of producing reliable, accountable, and human-understandable explanations across modern machine learning and deep learning applications.

Designed for undergraduate and postgraduate students, researchers, university faculty, AI engineers, software developers, data scientists, policymakers, and industry professionals, this book serves as a comprehensive guide to next-generation Explainable Artificial Intelligence.

What You’ll Learn

✔ Advanced Explainable AI (XAI)

✔ Local Interpretable Model-Agnostic Explanations (LIME)

✔ Integrated Gradients (IG)

✔ DeepLIFT

✔ Anchors Explanations

✔ Feature Interaction Analysis

✔ Concept Activation Vectors (TCAV)

✔ Perturbation-Based Attribution

✔ Attribution Method Comparison

✔ Decision Tree Explainability

✔ Random Forest Interpretation

✔ Gradient Boosting Explainability

✔ Support Vector Machine (SVM) Interpretation

✔ K-Nearest Neighbor (KNN) Explainability

✔ Feature Importance

✔ Partial Dependence Plots (PDP)

✔ Individual Conditional Expectation (ICE)

✔ Accumulated Local Effects (ALE)

✔ Deep Learning Interpretability

✔ Gradient-Based Explanations

✔ Saliency Maps

✔ SmoothGrad

✔ Grad-CAM

✔ Grad-CAM++

✔ Layer-wise Relevance Propagation (LRP)

✔ Deep Visualization

✔ Neural Embeddings

✔ Explainability for Transformer Models

✔ Attention Mechanisms

✔ Large Language Models (LLMs)

✔ Token Attribution

✔ Vision Explainability

✔ CNN Interpretability

✔ NLP Explainability

✔ Explainability Metrics

✔ Fidelity

✔ Stability

✔ Completeness

✔ Consistency

✔ Sensitivity Analysis

✔ Human-Centered Interpretability

✔ Benchmarking Explainability

✔ AI Fairness

✔ Bias Detection

✔ Bias Mitigation

✔ Trustworthy AI

✔ Adversarial Robustness

✔ Ethical Artificial Intelligence

✔ Formal Verification

✔ Symbolic Reasoning

✔ Model Checking

✔ Mechanistic Interpretability

✔ AI Alignment

✔ Human-Centered AI

Chapter 11

Attribution Methods Beyond Shapley

Part V – Interpretability in Machine Learning & Deep Learning

Chapter 12

Interpretability in Classical Machine Learning

Chapter 13

Explainability for Deep Learning Models

Chapter 14

Explainability in NLP & Computer Vision

Part VI – Evaluation, Ethics & Real-World Integration

Chapter 15

Evaluation Metrics for Explainability

Chapter 16

Fairness, Bias & Robustness

Chapter 17

Real-World Applications of Explainable Artificial Intelligence

Part VII – Advanced Topics & Future Directions

Chapter 18

Formal Verification & Logic-Based Explainability

Chapter 19

Explainability for Large Language Models

Chapter 20

Future of Explainable Artificial Intelligence

Who Should Read This Book?

This book is ideal for:

M.Tech Students
PhD Scholars
Artificial Intelligence Researchers
Machine Learning Engineers
Deep Learning Researchers
Data Scientists
AI Engineers
NLP Researchers
Large Language Model Researchers
Computer Vision Engineers
Software Engineers
Responsible AI Practitioners
Explainable AI Researchers
Healthcare AI Professionals
Financial AI Analysts
Robotics Engineers
University Faculty
Research Scientists
Government Policy Researchers
Competitive Examination Aspirants

Key Features

✅ Advanced Explainable AI techniques with mathematical foundations

✅ Comprehensive coverage of attribution methods beyond SHAP

✅ Deep Learning and Transformer explainability

✅ Explainability for Large Language Models (LLMs)

✅ Fairness, bias mitigation, and trustworthy AI

✅ Formal verification and symbolic reasoning for AI

✅ Real-world applications across healthcare, finance, robotics, law, manufacturing, and autonomous systems

✅ Human-centered and regulation-aware AI development

✅ Research-oriented content aligned with the latest advances in XAI

✅ Suitable for graduate studies, industrial AI development, and academic research

Why This Book?

Modern Artificial Intelligence systems are increasingly expected to be accurate, transparent, fair, accountable, and legally compliant. While many books introduce explainability concepts, few provide a complete mathematical and practical framework that spans attribution methods, deep learning interpretability, fairness evaluation, formal verification, and Large Language Model analysis.

This book bridges that gap by integrating advanced Explainable AI, causal reasoning, trustworthy AI, ethical AI principles, formal verification, and mechanistic interpretability into a unified learning resource. Readers gain the theoretical foundations and practical techniques needed to design AI systems that humans can understand, trust, and safely deploy in high-impact domains.

Whether you are conducting cutting-edge AI research, developing enterprise AI solutions, or studying Explainable Artificial Intelligence at the graduate level, this volume provides an authoritative guide to one of the fastest-growing fields in Artificial Intelligence.

Book Details

Title: Principles of Explainable Artificial Intelligence

Subtitle: Advanced Interpretability, Trustworthy AI & Large Language Model Explainability

Volume: Vol. 2

Author: Anshuman Mishra

Publisher: Anshuman Mishra

Publication Year: 2025

Language: English

Category: Artificial Intelligence, Explainable AI, Machine Learning, Deep Learning, Data Science, Responsible AI

Random Forest Explainability

Principles of Explainable Artificial Intelligence VOL-2