Corrective Machine Unlearning

arXiv:2402.14015v1 Announce Type: new
Abstract: Machine Learning models increasingly face data integrity challenges due to the use of large-scale training datasets drawn from the internet. We study what model developers can do if they detect that some data was manipulated or incorrect. Such manipulated data can cause adverse effects like vulnerability to backdoored samples, systematic biases, and in general, reduced accuracy on certain input domains. Often, all manipulated training samples are not known, and only a small, representative subset of …

abstract arxiv biases challenges cs.ai cs.cr cs.cv cs.lg data data integrity datasets developers effects face integrity internet machine machine learning machine learning models samples scale study training type unlearning vulnerability