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A Comprehensive Guide to Explainable AI: From Classical Models to LLMs

Weiche Hsieh, Ziqian Bi, Chuanqi Jiang, Junyu Liu, Benji Peng, Sen Zhang, Xuanhe Pan, Jiawei Xu, Jinlang Wang, Keyu Chen, Pohsun Feng, Yizhu Wen, Xinyuan Song, Tianyang Wang, Ming Liu, Junjie Yang, Ming Li, Bowen Jing, Jintao Ren, Junhao Song, Hong-Ming Tseng, Yichao Zhang, Lawrence K. Q. Yan, Qian Niu, Silin Chen, Yunze Wang, Chia Xin Liang

TL;DR

This work presents a holistic framework for Explainable AI, tracing the need for transparency from foundational concepts to cutting-edge methods for traditional models, deep networks, and LLMs. It systematically surveys interpretability theories, a broad suite of model- and post-hoc techniques (including SHAP, LIME, Grad-CAM, and attribution methods), and visual tools (PDPs, ICE, ALE, SHAP plots, and more). Through concrete Python examples and domain case studies, it demonstrates how to diagnose, explain, and trust AI decisions in healthcare, finance, and policy settings, while addressing evaluation metrics and ethical considerations. The compendium culminates with practical guidance on tools, frameworks, and future research directions, emphasizing responsible use of explanations, calibration of trust, and the integration of interpretability into regulatory and real-world AI deployments.

Abstract

Explainable Artificial Intelligence (XAI) addresses the growing need for transparency and interpretability in AI systems, enabling trust and accountability in decision-making processes. This book offers a comprehensive guide to XAI, bridging foundational concepts with advanced methodologies. It explores interpretability in traditional models such as Decision Trees, Linear Regression, and Support Vector Machines, alongside the challenges of explaining deep learning architectures like CNNs, RNNs, and Large Language Models (LLMs), including BERT, GPT, and T5. The book presents practical techniques such as SHAP, LIME, Grad-CAM, counterfactual explanations, and causal inference, supported by Python code examples for real-world applications. Case studies illustrate XAI's role in healthcare, finance, and policymaking, demonstrating its impact on fairness and decision support. The book also covers evaluation metrics for explanation quality, an overview of cutting-edge XAI tools and frameworks, and emerging research directions, such as interpretability in federated learning and ethical AI considerations. Designed for a broad audience, this resource equips readers with the theoretical insights and practical skills needed to master XAI. Hands-on examples and additional resources are available at the companion GitHub repository: https://github.com/Echoslayer/XAI_From_Classical_Models_to_LLMs.

A Comprehensive Guide to Explainable AI: From Classical Models to LLMs

TL;DR

This work presents a holistic framework for Explainable AI, tracing the need for transparency from foundational concepts to cutting-edge methods for traditional models, deep networks, and LLMs. It systematically surveys interpretability theories, a broad suite of model- and post-hoc techniques (including SHAP, LIME, Grad-CAM, and attribution methods), and visual tools (PDPs, ICE, ALE, SHAP plots, and more). Through concrete Python examples and domain case studies, it demonstrates how to diagnose, explain, and trust AI decisions in healthcare, finance, and policy settings, while addressing evaluation metrics and ethical considerations. The compendium culminates with practical guidance on tools, frameworks, and future research directions, emphasizing responsible use of explanations, calibration of trust, and the integration of interpretability into regulatory and real-world AI deployments.

Abstract

Explainable Artificial Intelligence (XAI) addresses the growing need for transparency and interpretability in AI systems, enabling trust and accountability in decision-making processes. This book offers a comprehensive guide to XAI, bridging foundational concepts with advanced methodologies. It explores interpretability in traditional models such as Decision Trees, Linear Regression, and Support Vector Machines, alongside the challenges of explaining deep learning architectures like CNNs, RNNs, and Large Language Models (LLMs), including BERT, GPT, and T5. The book presents practical techniques such as SHAP, LIME, Grad-CAM, counterfactual explanations, and causal inference, supported by Python code examples for real-world applications. Case studies illustrate XAI's role in healthcare, finance, and policymaking, demonstrating its impact on fairness and decision support. The book also covers evaluation metrics for explanation quality, an overview of cutting-edge XAI tools and frameworks, and emerging research directions, such as interpretability in federated learning and ethical AI considerations. Designed for a broad audience, this resource equips readers with the theoretical insights and practical skills needed to master XAI. Hands-on examples and additional resources are available at the companion GitHub repository: https://github.com/Echoslayer/XAI_From_Classical_Models_to_LLMs.

Paper Structure

This paper contains 566 sections, 80 equations, 50 figures.

Table of Contents

  1. Introduction
  2. Background and Importance of Explainable AI (XAI)
  3. Core Concepts and Definitions of XAI
  4. XAI, Transparency, Interpretability, and Fairness in AI
  5. Structure of the Book and Reader's Guide
  6. Theoretical Foundations of Explainable AI
  7. Why is Interpretability Needed? The AI Black Box Problem
  8. Trade-off Between Interpretability and Model Complexity
  9. Key Challenges in Achieving Interpretability
  10. Different Levels and Types of Interpretability
  11. Interpretability vs. Visualization
  12. Intrinsic Interpretability vs. Post-hoc Interpretability
  13. Interpretability of Traditional Machine Learning Models
  14. Differences Between Interpretable and Non-interpretable Models
  15. The Interpretability-Complexity Continuum
  16. ...and 551 more sections

Figures (50)

  • Figure 1: SHAP Summary Plot for Feature Importance Visualization
  • Figure 2: The interpretability-complexity continuum of common machine learning models rudin2019stop.
  • Figure 3: Visualization of Feature Importance in a Decision Tree
  • Figure 4: Comparison of True vs Predicted House Prices Using Linear Regression
  • Figure 5: Logistic Regression Model Predictions for Customer Churn
  • ...and 45 more figures