Machine Unlearning : Principles, Methods, and Evolving Frontiers

This book explores machine unlearning, a vital AI field for selectively removing learned data from models. Covering advanced techniques, real-world case studies, and ethical considerations like GDPR compliance, it equips readers to implement responsible AI systems while addressing data privacy and societal challenges.

This book explores one of the most critical and emerging fields in artificial intelligence (AI): machine unlearning. As data privacy concerns grow and regulations like GDPR (General Data Protection Regulation) demand compliance, this book provides a comprehensive guide to selectively removing learned information from machine learning models without sacrificing performance or requiring complete retraining. Covering foundational principles, advanced algorithms, benchmarking tools, and real-world case studies in healthcare, finance, and social media, the book bridges the gap between theory and practice. It also addresses ethical, legal, and societal implications, offering insights into creating trustworthy AI systems. This book is an essential resource for understanding and implementing machine unlearning in the era of responsible AI.