Data Removal

Data removal, or machine unlearning, focuses on efficiently removing the influence of specific data points from trained machine learning models, primarily to comply with privacy regulations and user rights. Current research emphasizes developing algorithms that achieve this without requiring complete model retraining, exploring techniques like twin machine unlearning, adapter-based methods for LLMs, and approaches that leverage model augmentation or re-weighting of remaining data. This field is crucial for responsible AI development, enabling better data privacy protection and fostering trust in machine learning systems across various applications, including recommendation systems and federated learning.