In order to extend machine unlearning techniques to handle user-level or sample-level forgetting requests, the methods need to be adapted to operate at a more granular level of data removal. This would involve developing algorithms that can selectively forget specific user-contributed data or individual data samples within a category. One approach could be to incorporate user identifiers or unique data sample identifiers into the unlearning process, allowing the model to target and remove specific user data or samples upon request.
To implement sample-level forgetting, the machine unlearning methods would need to be enhanced to identify and isolate individual data samples for removal. This could involve developing mechanisms to track and manipulate the representations of individual samples within the model, enabling precise deletion of specific samples without affecting the overall model performance. Techniques such as instance-based unlearning or selective weight updates could be explored to achieve sample-level forgetting while maintaining the integrity of the model.
By extending machine unlearning techniques to handle user-level or sample-level forgetting requests, AI systems can offer more personalized and customizable data privacy options to users, enhancing trust and transparency in AI services.