Corrective Machine Unlearning
Keywords: Machine Unlearning, Data Manipulation, Poisoning
TL;DR: We motivate unlearning wrong data to mitigate its adverse effects, and show existing methods perform poorly due to significantly different requirements from privacy.
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 the affected data is flagged.
We formalize “Corrective Machine Unlearning” as the problem of mitigating the impact of data affected by unknown manipulations on a trained model, possibly knowing only a subset of impacted samples. We demonstrate that the problem of corrective unlearning has significantly different requirements from traditional privacy-oriented unlearning. We find most existing unlearning methods, including the gold-standard retraining-from-scratch, require most of the manipulated data to be identified for effective corrective unlearning. However, one approach, SSD, achieves limited success in unlearning adverse effects with just a small portion of the manipulated samples, showing the tractability of this setting. We hope our work spurs research towards developing better methods for corrective unlearning, and offers practitioners a new strategy to handle data integrity challenges arising from web-scale training. We release our code at https://github.com/drimpossible/corrective-unlearning-bench.
Primary Subject Area: Impact of data bias, variance, and drifts
Paper Type: Research paper: up to 8 pages
Participation Mode: In-person
Confirmation: I have read and agree with the workshop's policy on behalf of myself and my co-authors.
Submission Number: 17
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