On the limitation of evaluating machine unlearning using only a single training seed

A recent study discusses the limitations of evaluating machine unlearning (MU) by relying on a single training seed. MU is crucial for removing specific data influences from models without the need for extensive retraining. The research emphasizes the importance of conducting empirical assessments with multiple independent runs to ensure that comparisons are valid and representative. This work is significant as it highlights the need for robust evaluation methods in the evolving field of machine learning, ensuring that algorithms can be effectively tested and improved.