Knowledge Removal

Knowledge removal, also known as machine unlearning, focuses on selectively deleting specific information from trained machine learning models, addressing privacy concerns and regulatory compliance like the "Right to Be Forgotten." Current research explores efficient algorithms for this process, including methods that leverage layer-wise pruning, optimization-based techniques, and novel approaches for Bayesian inference models. The ability to effectively remove knowledge is crucial for responsible AI development, mitigating risks associated with biased data, data breaches, and unwanted information leakage in various applications.