Improving LIME Robustness with Smarter Locality Sampling
Abstract
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.
Cite
@article{arxiv.2006.12302,
title = {Improving LIME Robustness with Smarter Locality Sampling},
author = {Sean Saito and Eugene Chua and Nicholas Capel and Rocco Hu},
journal= {arXiv preprint arXiv:2006.12302},
year = {2021}
}
Comments
AdvML '20: Workshop on Adversarial Learning Methods for Machine Learning and Data Mining, August 24, 2020, San Diego, CA. 5 pages, 2 figures