  Machine unlearning (MU) is gaining increasing attention due to the need to
remove or modify predictions made by machine learning (ML) models. While
training models have become more efficient and accurate, the importance of
unlearning previously learned information has become increasingly significant
in fields such as privacy, security, and fairness. This paper presents a
comprehensive survey of MU, covering current state-of-the-art techniques and
approaches, including data deletion, perturbation, and model updates. In
addition, commonly used metrics and datasets are also presented. The paper also
highlights the challenges that need to be addressed, including attack
sophistication, standardization, transferability, interpretability, training
data, and resource constraints. The contributions of this paper include
discussions about the potential benefits of MU and its future directions.
Additionally, the paper emphasizes the need for researchers and practitioners
to continue exploring and refining unlearning techniques to ensure that ML
models can adapt to changing circumstances while maintaining user trust. The
importance of unlearning is further highlighted in making Artificial
Intelligence (AI) more trustworthy and transparent, especially with the
increasing importance of AI in various domains that involve large amounts of
personal user data.
