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.

翻译：机器遗忘（MU）因需要移除或修改机器学习（ML）模型所做出的预测而日益受到关注。尽管模型的训练已变得更加高效和准确，但遗忘先前所学信息的重要性在隐私、安全和公平等领域愈发凸显。本文对MU进行了全面综述，涵盖了当前最先进的技术和方法，包括数据删除、扰动和模型更新。此外，还介绍了常用的评估指标和数据集。本文同时指出了需要应对的挑战，包括攻击的复杂性、标准化、可迁移性、可解释性、训练数据以及资源限制。本文的贡献包括对MU潜在益处及其未来方向的讨论。此外，本文强调研究人员和实践者需要持续探索和完善遗忘技术，以确保ML模型能够适应变化的环境，同时维护用户信任。随着人工智能（AI）在涉及大量个人用户数据的各个领域日益重要，遗忘对于提升AI的可信度和透明度的重要性尤为凸显。