Machine Unlearning
Principles, Methods, and Evolving Frontiers
Will be published approx. on 13. October 2026
136 pages
E-Book
978-1-040-91023-8 (ISBN)
Description
This book explores one of the most critical and emerging fields in artificial intelligence, machine unlearning. As data privacy concerns grow and regulations like GDPR 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.
More details
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Illustrations
1 Tables, black and white; 10 Line drawings, black and white; 10 Illustrations, black and white
ISBN-13
978-1-040-91023-8 (9781040910238)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Bitan Misra | Sayan Chakraborty | Nilanjan Dey
Machine Unlearning
Principles, Methods, and Evolving Frontiers
Book
approx. 10/2026
1st Edition
CRC Press
€107.50
Not yet published
Persons
Bitan Misra is currently working as an Assistant Professor, in Dept. of CSE, Techno International New Town, Kolkata, India. She received her B. Tech and M. Tech dual degree in Electronics and Telecommunication Engineering from KIIT University, Bhubaneswar, India in 2018. She received her Ph.D. in 2022 from National Institute of Technology, Durgapur, India. She has published almost 40 research papers in various international journals and conferences and is the author of 5 books. Her main research interests include optimization techniques, deep learning, evolutionary algorithms and soft computing techniques. She has worked as a reviewer in several national and international journals and conferences. She is an Associate Editor of International Journal of Ambient Computing and Intelligence, IGI Global. She is a member of IEEE and Internet Society.
Dr. Sayan Chakraborty (Senior Member, IEEE) is currently working as an Assistant Professor in the Department of Computer Science and Technology at JIS College of Engineering, Kalyani, West Bengal, India. He completed his Ph.D. in Image Registration from Sikkim Manipal University in 2023. He completed his M.Tech in Computer Science and Engineering from JIS College of Engineering and also obtained his B.Tech from the same institution. He has over 12 years of academic experience. His research interests include digital image processing, nature-inspired algorithms, and machine learning. He has published 92 research papers in international journals, book chapters, and conference proceedings on topics including optimization, artificial intelligence, pattern recognition, and digital image processing. Dr. Chakraborty has authored and edited 4 books published by Springer, Academia Press, and CRC Press. He is currently serving as an Associate Editor of the International Journal of Ambient Computing and Intelligence (IGI Global) and is also an Editorial Board Member of the International Journal of Rough Sets and Data Analysis (IJRSDA), IGI Global.
Nilanjan Dey (Senior Member, IEEE) received the B.Tech. and M.Tech. in information technology from West Bengal Board of Technical University and the Ph.D. in electronics and telecommunication engineering from Jadavpur University, Kolkata, India, in 2005, 2011, and 2015. Currently, he is a professor with Techno International New Town, Kolkata, and a visiting fellow of the University of Reading, UK. He is the Editor-in-Chief of the International Journal of Ambient Computing and Intelligence, Associate Editor of IEEE Transactions on Technology and Society, and series Co-Editor of Springer Tracts in Nature-Inspired Computing and Data-Intensive Research (Springer Nature) and Advances in Ubiquitous Sensing Applications for Healthcare (Elsevier). He is also on the Editorial Board of IEEE Data Descriptions. He is a Fellow of IETE and member of IE, ISOC, etc.
Dr. Sayan Chakraborty (Senior Member, IEEE) is currently working as an Assistant Professor in the Department of Computer Science and Technology at JIS College of Engineering, Kalyani, West Bengal, India. He completed his Ph.D. in Image Registration from Sikkim Manipal University in 2023. He completed his M.Tech in Computer Science and Engineering from JIS College of Engineering and also obtained his B.Tech from the same institution. He has over 12 years of academic experience. His research interests include digital image processing, nature-inspired algorithms, and machine learning. He has published 92 research papers in international journals, book chapters, and conference proceedings on topics including optimization, artificial intelligence, pattern recognition, and digital image processing. Dr. Chakraborty has authored and edited 4 books published by Springer, Academia Press, and CRC Press. He is currently serving as an Associate Editor of the International Journal of Ambient Computing and Intelligence (IGI Global) and is also an Editorial Board Member of the International Journal of Rough Sets and Data Analysis (IJRSDA), IGI Global.
Nilanjan Dey (Senior Member, IEEE) received the B.Tech. and M.Tech. in information technology from West Bengal Board of Technical University and the Ph.D. in electronics and telecommunication engineering from Jadavpur University, Kolkata, India, in 2005, 2011, and 2015. Currently, he is a professor with Techno International New Town, Kolkata, and a visiting fellow of the University of Reading, UK. He is the Editor-in-Chief of the International Journal of Ambient Computing and Intelligence, Associate Editor of IEEE Transactions on Technology and Society, and series Co-Editor of Springer Tracts in Nature-Inspired Computing and Data-Intensive Research (Springer Nature) and Advances in Ubiquitous Sensing Applications for Healthcare (Elsevier). He is also on the Editorial Board of IEEE Data Descriptions. He is a Fellow of IETE and member of IE, ISOC, etc.
Content
Preface
Chapter 1
1. Introduction to Machine Unlearning
1.1. Potential Approaches
1.2. Motivation for Unlearning
1.3. Unlearning Framework
1.4. Unlearning requests
1.5. Design Requirements
1.6. Machine unlearning applications
Chapter 2
2. Technological Approaches to Unlearning
2.1. Algorithmic Strategies for Unlearning
2.2. Data Deletion Techniques
2.3. Model Retraining vs. Unlearning
2.4. Naive Unlearning Approaches
2.4.1. Retraining from Scratch
2.4.2. Model Reset and Data Exclusion
2.5. Approximate Unlearning Methods
2.5.1. Fine-tuning and Gradient Reversal
2.5.2. Knowledge Distillation and Substitution
2.6. Exact Unlearning Methods
Chapter 3
3. Machine Unlearning in GenAI and LLM
3.1 Overview of unlearning challenges in Generative AI and LLMs.
3.2. Unlearning in Generative AI
3.2.1. Retraining from Scratch
3.2.2. Knowledge Distillation
3.2.3. Gradient reversal and model editing
3.3. Unlearning in Large Language Models (LLMs)
3.3.1. Parameter-tuning Approaches
3.3.2. Parameter-Agnostic Methods
3.3.3. LLM Unlearning Taxonomy
3.4. Comparative Insights
Chapter 4
4. Benchmark Datasets and Experimental Frameworks
4.1. Benchmark datasets and experimental frameworks
4.1.1. TOFU
4.1.2. WMDP
4.1.3. CIFAR-10
4.1.4. MNIST
4.1.5. Fashion MNIST
4.1.6. UTKFace
4.1.7. Machine Unlearning for Facial Age Classifier (MUFAC)
4.1.8. Machine Unlearning for Celebrity Attribute Classifier (MUCAC)
4.2. Unlearning-Specific Benchmarks
4.2.1. Machine Unlearning Six-way Evaluation (MUSE)
4.2.2. Real-World Knowledge Unlearning (RWKU)
4.3. Unlearning Efficiency
4.4. Model Utility
4.5. Machine Unlearning Tools and Frameworks
4.5.1. Design requirements
4.5.2. Validation of machine unlearning
4.5.3. Metrics
4.6. Unlearning verification
4.6.1. Feature Injection Test
4.6.2. Forgetting Measuring
4.6.3. Information leakage
4.6.4. Membership inference attacks
4.6.5. Backdoor attacks
4.6.6. Slow-down attacks
4.6.7. Interclass confusion test
4.6.8. Federated verification
4.6.9. Cryptographic proofs
Chapter 5
5. Case Studies in Machine Unlearning
5.1. Unlearning in Healthcare
5.1.1. Unlearning Versus Deskilling
5.1.2. Technical Challenges
5.1.3. Limitations of Machine Unlearning in Healthcare
5.1.4. Unlearning in Digital Healthcare
5.2. Unlearning in Financial Services and Data Retention
5.3. Machine unlearning in social media and user data control
5.3.1. Challenges and Future Directions
5.4. Social Bias Mitigation in Language Models via Machine Unlearning
5.4.1. Machine unlearning techniques
Chapter 6
6. Data Privacy Ethical Implications
6.1. Right to be Forgotten (RTBF)
6.1.1. Reasons behind the increase in RTBF
6.1.2. Issues of LLMs related to Personal Data
6.1.3. The origin and development of the right to be forgotten
6.2. Toward a Multifaceted Strategy
6.3. Recommendations to maintain data privacy
Chapter 7
7. Challenges in Applying RTBF to AI Systems
7.1. Technical challenges
7.2. Conceptual challenges
7.3. Ethical Concerns Related to AI Inferences
7.4. Jurisdictional and cross-border data flows
7.5. Commercial Interests and Data Monetization
7.6. Societal and Cultural Implications
7.7. Stochasticity of training
7.8. Incrementality of training
7.9. Catastrophic unlearning.
7.10. Challenges of Applying RTBF to LLMs
7.11. Computational expense
7.12. Loss of important information
7.13. Lack interpretability
7.14. Enhancing Public Understanding of RTBF and AI
7.15. Balancing Privacy Rights with the Benefits of AI
7.16. Hyperparameter search
7.17. Uncertainty
Chapter 8
8. Conclusions and Future Research Directions
8.1. Future research trajectories
8.1.1. Influence functions are the dominant methods
8.1.2. Reachability of the model parameters
8.1.3. Unlearning verification (Data auditing)
8.1.4. Federated unlearning
8.1.5. Model repair by unlearning
8.1.6. Proposed Changes to the GDPR and CCPA to Better Address the AI Challenge
8.1.7. Unlearning for Diverse Data Structures
8.1.8. Unlearning for Nonconvex Models
8.1.9. User-Specified Granularity of Unlearning
8.1.10. Privacy Assurance for Unlearning
8.1.11. Quantitative evaluation metrics
8.1.12. Adversarial Machine Unlearning
8.1.13. Interpretable Machine Unlearning
8.1.14. Causality in Machine Unlearning
8.1.15. Verifiable Machine Unlearning
8.2. Summary
Chapter 1
1. Introduction to Machine Unlearning
1.1. Potential Approaches
1.2. Motivation for Unlearning
1.3. Unlearning Framework
1.4. Unlearning requests
1.5. Design Requirements
1.6. Machine unlearning applications
Chapter 2
2. Technological Approaches to Unlearning
2.1. Algorithmic Strategies for Unlearning
2.2. Data Deletion Techniques
2.3. Model Retraining vs. Unlearning
2.4. Naive Unlearning Approaches
2.4.1. Retraining from Scratch
2.4.2. Model Reset and Data Exclusion
2.5. Approximate Unlearning Methods
2.5.1. Fine-tuning and Gradient Reversal
2.5.2. Knowledge Distillation and Substitution
2.6. Exact Unlearning Methods
Chapter 3
3. Machine Unlearning in GenAI and LLM
3.1 Overview of unlearning challenges in Generative AI and LLMs.
3.2. Unlearning in Generative AI
3.2.1. Retraining from Scratch
3.2.2. Knowledge Distillation
3.2.3. Gradient reversal and model editing
3.3. Unlearning in Large Language Models (LLMs)
3.3.1. Parameter-tuning Approaches
3.3.2. Parameter-Agnostic Methods
3.3.3. LLM Unlearning Taxonomy
3.4. Comparative Insights
Chapter 4
4. Benchmark Datasets and Experimental Frameworks
4.1. Benchmark datasets and experimental frameworks
4.1.1. TOFU
4.1.2. WMDP
4.1.3. CIFAR-10
4.1.4. MNIST
4.1.5. Fashion MNIST
4.1.6. UTKFace
4.1.7. Machine Unlearning for Facial Age Classifier (MUFAC)
4.1.8. Machine Unlearning for Celebrity Attribute Classifier (MUCAC)
4.2. Unlearning-Specific Benchmarks
4.2.1. Machine Unlearning Six-way Evaluation (MUSE)
4.2.2. Real-World Knowledge Unlearning (RWKU)
4.3. Unlearning Efficiency
4.4. Model Utility
4.5. Machine Unlearning Tools and Frameworks
4.5.1. Design requirements
4.5.2. Validation of machine unlearning
4.5.3. Metrics
4.6. Unlearning verification
4.6.1. Feature Injection Test
4.6.2. Forgetting Measuring
4.6.3. Information leakage
4.6.4. Membership inference attacks
4.6.5. Backdoor attacks
4.6.6. Slow-down attacks
4.6.7. Interclass confusion test
4.6.8. Federated verification
4.6.9. Cryptographic proofs
Chapter 5
5. Case Studies in Machine Unlearning
5.1. Unlearning in Healthcare
5.1.1. Unlearning Versus Deskilling
5.1.2. Technical Challenges
5.1.3. Limitations of Machine Unlearning in Healthcare
5.1.4. Unlearning in Digital Healthcare
5.2. Unlearning in Financial Services and Data Retention
5.3. Machine unlearning in social media and user data control
5.3.1. Challenges and Future Directions
5.4. Social Bias Mitigation in Language Models via Machine Unlearning
5.4.1. Machine unlearning techniques
Chapter 6
6. Data Privacy Ethical Implications
6.1. Right to be Forgotten (RTBF)
6.1.1. Reasons behind the increase in RTBF
6.1.2. Issues of LLMs related to Personal Data
6.1.3. The origin and development of the right to be forgotten
6.2. Toward a Multifaceted Strategy
6.3. Recommendations to maintain data privacy
Chapter 7
7. Challenges in Applying RTBF to AI Systems
7.1. Technical challenges
7.2. Conceptual challenges
7.3. Ethical Concerns Related to AI Inferences
7.4. Jurisdictional and cross-border data flows
7.5. Commercial Interests and Data Monetization
7.6. Societal and Cultural Implications
7.7. Stochasticity of training
7.8. Incrementality of training
7.9. Catastrophic unlearning.
7.10. Challenges of Applying RTBF to LLMs
7.11. Computational expense
7.12. Loss of important information
7.13. Lack interpretability
7.14. Enhancing Public Understanding of RTBF and AI
7.15. Balancing Privacy Rights with the Benefits of AI
7.16. Hyperparameter search
7.17. Uncertainty
Chapter 8
8. Conclusions and Future Research Directions
8.1. Future research trajectories
8.1.1. Influence functions are the dominant methods
8.1.2. Reachability of the model parameters
8.1.3. Unlearning verification (Data auditing)
8.1.4. Federated unlearning
8.1.5. Model repair by unlearning
8.1.6. Proposed Changes to the GDPR and CCPA to Better Address the AI Challenge
8.1.7. Unlearning for Diverse Data Structures
8.1.8. Unlearning for Nonconvex Models
8.1.9. User-Specified Granularity of Unlearning
8.1.10. Privacy Assurance for Unlearning
8.1.11. Quantitative evaluation metrics
8.1.12. Adversarial Machine Unlearning
8.1.13. Interpretable Machine Unlearning
8.1.14. Causality in Machine Unlearning
8.1.15. Verifiable Machine Unlearning
8.2. Summary
