Preface

Explainable AI (XAI) is an emerging field for bringing artificial intelligence (AI) closer to non-technical end-users. XAI promises to make machine learning (ML) models transparent, and trustworthy and promote AI adoption for industrial and research use-cases.

This book is designed with a unique blend of industrial and academic research perspectives for gaining practical skills in XAI. ML/AI experts working with data science, ML, deep learning, and AI will be able to put their knowledge to work with this practical guide to XAI for bridging the gap between AI and the end-user. The book provides a hands-on approach for implementation and associated methodologies of XAI that will have you up-and-running, and productive in no time.

Initially, you will get a conceptual understanding of XAI and why it's needed. Then, you will get the necessary practical experience of utilizing XAI in the AI/ML problem-solving process by making use of state-of-the-art methods and frameworks. Finally, you will get the necessary guidelines to take XAI to the next step and bridge the existing gaps between AI and end-users.

By the end of this book, you will be able to implement XAI methods and approaches using Python to solve industrial problems, address the key pain points encountered, and follow the best practices in the AI/ML life cycle.