  Explainability algorithms such as LIME have enabled machine learning systems
to adopt transparency and fairness, which are important qualities in commercial
use cases. However, recent work has shown that LIME's naive sampling strategy
can be exploited by an adversary to conceal biased, harmful behavior. We
propose to make LIME more robust by training a generative adversarial network
to sample more realistic synthetic data which the explainer uses to generate
explanations. Our experiments demonstrate that our proposed method demonstrates
an increase in accuracy across three real-world datasets in detecting biased,
adversarial behavior compared to vanilla LIME. This is achieved while
maintaining comparable explanation quality, with up to 99.94\% in top-1
accuracy in some cases.
