Explainable AI : foundations, methodologies and applications / Mayuri Mehta, Vasile Palade, Indranath Chatterjee, editors.

Intro
Preface
Contents
Contributors
Abbreviations
1 Black Box Models for eXplainable Artificial Intelligence
1.1 Introduction to Machine Learning
1.1.1 Motivation
1.1.2 Scope of the Paper
1.2 Importance of Cyber Security in eXplainable Artificial Intelligence
1.2.1 Importance of Trustworthiness
1.3 Deep Learning (DL) Methods Contribute to XAI
1.4 Intrusion Detection System
1.4.1 Classification of Intrusion Detection System
1.5 Applications of Cyber Security and XAI
1.6 Comparison of XAI Using Black Box Methods
1.7 Conclusion
References

2 Fundamental Fallacies in Definitions of Explainable AI: Explainable to Whom and Why?
2.1 Introduction
2.1.1 A Short History of Explainable AI
2.1.2 Diversity of Motives for Creating Explainable AI
2.1.3 Internal Inconsistency of Motives for Creating XAI
2.1.4 The Contradiction Between the Motives for Creating Explainable AI
2.1.5 Paradigm Shift of Explainable Artificial Intelligence
2.2 Proposed AI Model
2.2.1 The Best Way to Optimize the Interaction Between Human and AI
2.2.2 Forecasts Are not Necessarily Useful Information

2.2.3 Criteria for Evaluating Explanations
2.2.4 Explainable to Whom and Why?
2.3 Proposed Architecture
2.3.1 Fitness Function for Explainable AI
2.3.2 Deep Neural Network is Great for Explainable AI
2.3.3 The More Multitasking the Better
2.3.4 How to Collect Multitasking Datasets
2.3.5 Proposed Neural Network Architecture
2.4 Conclusions
References
3 An Overview of Explainable AI Methods, Forms and Frameworks
3.1 Introduction
3.2 XAI Methods and Their Classifications
3.2.1 Based on the Scope of Explainability
3.2.2 Based on Implementation

3.2.3 Based on Applicability
3.2.4 Based on Explanation Level
3.3 Forms of Explanation
3.3.1 Analytical Explanation
3.3.2 Visual Explanation
3.3.3 Rule-Based Explanation
3.3.4 Textual Explanation
3.4 Frameworks for Model Interpretability and Explanation
3.4.1 Explain like I'm 5
3.4.2 Skater
3.4.3 Local Interpretable Model-Agnostic Explanations
3.4.4 Shapley Additive Explanations
3.4.5 Anchors
3.4.6 Deep Learning Important Features
3.5 Conclusion and Future Directions
References

4 Methods and Metrics for Explaining Artificial Intelligence Models: A Review
4.1 Introduction
4.1.1 Bringing Explainability to AI Decision-Need for Explainable AI
4.2 Taxonomy of Explaining AI Decisions
4.3 Methods of Explainable Artificial Intelligence
4.3.1 Techniques of Explainable AI
4.3.2 Stages of AI Explainability
4.3.3 Types of Post-model Explaination Methods
4.4 Metrics for Explainable Artificial Intelligence
4.4.1 Evaluation Metrics for Explaining AI Decisions
4.5 Use-Case: Explaining Deep Learning Models Using Grad-CAM