Efficient Repair of Polluted Machine Learning Systems via Causal Unlearning

Abstract

Machine learning systems, though being successful in many real-world applications, are known to remain prone to errors and attacks. A major attack, called data pollution, injects maliciously crafted training data samples into the training set, causing the system to learn an incorrect model and subsequently misclassify testing samples. A natural solution to a data pollution attack is to remove the polluted data from the training set and relearn a clean model. Unfortunately, the training set of a real-world machine learning system can contain millions of samples; it is thus hopeless for an administrator to manually inspect all of them to weed out the polluted ones.