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.
eBook - ePub
Machine Unlearning
Principles, Methods, and Evolving Frontiers
- 105 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
eBook - ePub
Machine Unlearning
Principles, Methods, and Evolving Frontiers
About this book
Information
Table of contents
- Cover
- Half Title
- Title
- Copyright
- Contents
- Author Biographies
- Preface
- Chapter 1 Introduction to Machine Unlearning
- Chapter 2 Technological Approaches to Unlearning
- Chapter 3 Machine Unlearning in Generative AI and LLMs
- Chapter 4 Benchmark Datasets and Experimental Frameworks
- Chapter 5 Case Studies in Machine Unlearning
- Chapter 6 Data Privacy Ethical Implications
- Chapter 7 Challenges in Applying RTBF to AI Systems
- Chapter 8 Conclusions and Future Research Directions
- Index
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Yes, you can access Machine Unlearning by Bitan Misra,Sayan Chakraborty,Nilanjan Dey in PDF and/or ePUB format, as well as other popular books in Computer Science & Artificial Intelligence (AI) & Semantics. We have over 1.5 million books available in our catalogue for you to explore.