  The Right to be Forgotten is a core principle outlined by regulatory
frameworks such as the EU's General Data Protection Regulation (GDPR). This
principle allows individuals to request that their personal data be deleted
from deployed machine learning models. While "forgetting" can be naively
achieved by retraining on the remaining dataset, it is computationally
expensive to do to so with each new request. As such, several machine
unlearning methods have been proposed as efficient alternatives to retraining.
These methods aim to approximate the predictive performance of retraining, but
fail to consider how unlearning impacts other properties critical to real-world
applications such as fairness. In this work, we demonstrate that most efficient
unlearning methods cannot accommodate popular fairness interventions, and we
propose the first fair machine unlearning method that can efficiently unlearn
data instances from a fair objective. We derive theoretical results which
demonstrate that our method can provably unlearn data and provably maintain
fairness performance. Extensive experimentation with real-world datasets
highlight the efficacy of our method at unlearning data instances while
preserving fairness.
