Can Your Personal Data Disappear from AI’s Memory? :Machine Unlearning (03/28)

Machine Unlearning?

Machine unlearning refers to the process in machine learning where given data or patterns are deliberately removed or forgotten. This could be necessary for various reasons such as data protection, privacy concerns, or model updates. While some machine learning models may naturally forget previous data or patterns as they learn new ones, there are instances where explicit control over this forgetting process is necessary.

For instance, a company may need to delete customer data to protect personal information. In such cases, there is a need for techniques in machine learning to delete data from models trained using that data. Through this process, the model can forget previously learned data or patterns and be ready to learn from new data.

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Why machine unlearning needed?

Privacy Protection: Privacy protection is one of the critical issues in the digital age. To safeguard user’s personal information, machine learning models must be capable of deleting and forgetting user data. Particularly, certain regulations mandate the deletion of user’s personal information when it is no longer necessary. To address this appropriately, machine learning models need functionalities to forget such information as well.

Copyright Infringement Prevention: Machine learning models are often trained using large volumes of data. However, some of this data may include copyrighted material. To prevent copyright infringement, it is necessary to delete any copyrighted material from the data used to train the model and retrain the model accordingly if copyrighted material is detected among the data it has learned from.

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