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.

Written for beginners and advanced machine learning users, including engineers and researchers on AI and applications Covers concepts such as black box models, transparency, interpretable machine learning and explanations Presents evaluation methods and metrics, ethical, legal, and social issues, and applications and examples of XAI

Inhalt
Black Box Models for eXplainable Artificial Intelligence.- Fundamental Fallacies in Definitions of Explainable AI: Explainable to Whom and Why?.- An Overview of Explainable AI Methods, Forms and Frameworks.