  This survey investigates the multifaceted nature of forgetting in machine
learning, drawing insights from neuroscientific research that posits forgetting
as an adaptive function rather than a defect, enhancing the learning process
and preventing overfitting. This survey focuses on the benefits of forgetting
and its applications across various machine learning sub-fields that can help
improve model performance and enhance data privacy. Moreover, the paper
discusses current challenges, future directions, and ethical considerations
regarding the integration of forgetting mechanisms into machine learning
models.
