  Explainability is a gateway between Artificial Intelligence and society as
the current popular deep learning models are generally weak in explaining the
reasoning process and prediction results. Local Interpretable Model-agnostic
Explanation (LIME) is a recent technique that explains the predictions of any
classifier faithfully by learning an interpretable model locally around the
prediction. However, the sampling operation in the standard implementation of
LIME is defective. Perturbed samples are generated from a uniform distribution,
ignoring the complicated correlation between features. This paper proposes a
novel Modified Perturbed Sampling operation for LIME (MPS-LIME), which is
formalized as the clique set construction problem. In image classification,
MPS-LIME converts the superpixel image into an undirected graph. Various
experiments show that the MPS-LIME explanation of the black-box model achieves
much better performance in terms of understandability, fidelity, and
efficiency.
