This book presents an overview and several applications of explainable artificial intelligence (XAI). It covers different aspects related to explainable artificial intelligence, such as the need to make the AI models interpretable, how black box machine/deep learning models can be understood using various XAI methods, different evaluation metrics for XAI, human-centered explainable AI, and applications of explainable AI in health care, security surveillance, transportation, among other areas.

The book is suitable for students and academics aiming to build up their background on explainable AI and can guide them in making machine/deep learning models more transparent. The book can be used as a reference book for teaching a graduate course on artificial intelligence, applied machine learning, or neural networks. Researchers working in the area of AI can use this book to discover the recent developments in XAI. Besides its use in academia, this book could be used by practitioners in AI industries, healthcare industries, medicine, autonomous vehicles, and security surveillance, who would like to develop AI techniques and applications with explanations.

Table of Contents

1 Black Box Models for eXplainable Artificial Intelligence

2 Fundamental Fallacies in Definitions of Explainable Al: Explainable to Whom and Why?

3 An Overview of Explainable Al Methods, Forms and Frameworks

4 Methods and Metrics for Explaining Artificial Intelligence Models: A Review

5 Evaluation Measures and Applications for Explainable Al

6 Explainable Al and Its Applications in Healthcare

7 Explainable Al Driven Applications for Patient Care and Treatment

8 Explainable Machine Learning for Autonomous Vehicle Positioning Using SHAP

9 A Smart System for the Assessment of Genuineness or Trustworthiness of the Tip-Off Using Audio Signals: An Explainable Al Approach

10 Face Mask Detection Based Entry Control Using XAI and loT

11 Human-Al Interfaces are a Central Component of Trustworthy Al