Forgetting Similar Samples: Can Machine Unlearning Do it Better?

Machine unlearning is a process that allows AI models to forget the influence of specific training samples, which is important for privacy and safety. Researchers tested whether existing unlearning methods actually work when the training dataset contains similar samples to the ones being removed, and found that most methods fail to completely eliminate a target sample's influence even when compared to retraining from scratch (rebuilding the model from the beginning with the unwanted sample excluded).