Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning

This book is a great guide to anyonewho is new to the field of XAI or is already familiar with the field and is willing to expand their knowledge. A comprehensive review of the state-of-the-art Explainable AI methods starting from visualization, interpretable methods, local and global explanations,...

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Bibliographic Details
Main Authors: Kamath, Uday, Liu, John (Author)
Format: eBook
Published: Cham Springer International Publishing 2021, 2021
Edition:1st ed. 2021
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Table of Contents:
  • 1. Introduction to Interpretability and Explainability
  • 2. Pre-Model Interpretability and Explainability
  • 3. Model Visualization Techniques and Traditional Interpretable Algorithms
  • 4. Model Interpretability: Advances in Interpretable Machine Learning
  • 5. Post-hoc Interpretability and Explanations
  • 6. Explainable Deep Learning
  • 7. Explainability in Time Series Forecasting, Natural Language Processing, and Computer Vision
  • 8. XAI: Challenges and Future