  In current AI era, users may request AI companies to delete their data from
the training dataset due to the privacy concerns. As a model owner, retraining
a model will consume significant computational resources. Therefore, machine
unlearning is a new emerged technology to allow model owner to delete requested
training data or a class with little affecting on the model performance.
However, for large-scaling complex data, such as image or text data, unlearning
a class from a model leads to a inferior performance due to the difficulty to
identify the link between classes and model. An inaccurate class deleting may
lead to over or under unlearning. In this paper, to accurately defining the
unlearning class of complex data, we apply the definition of Concept, rather
than an image feature or a token of text data, to represent the semantic
information of unlearning class. This new representation can cut the link
between the model and the class, leading to a complete erasing of the impact of
a class. To analyze the impact of the concept of complex data, we adopt a
Post-hoc Concept Bottleneck Model, and Integrated Gradients to precisely
identify concepts across different classes. Next, we take advantage of data
poisoning with random and targeted labels to propose unlearning methods. We
test our methods on both image classification models and large language models
(LLMs). The results consistently show that the proposed methods can accurately
erase targeted information from models and can largely maintain the performance
of the models.
