  While deep learning makes significant achievements in Artificial Intelligence
(AI), the lack of transparency has limited its broad application in various
vertical domains. Explainability is not only a gateway between AI and real
world, but also a powerful feature to detect flaw of the models and bias of the
data. Local Interpretable Model-agnostic Explanation (LIME) is a
widely-accepted technique that explains the prediction of any classifier
faithfully by learning an interpretable model locally around the predicted
instance. As an extension of LIME, this paper proposes an high-interpretability
and high-fidelity local explanation method, known as Local Explanation using
feature Dependency Sampling and Nonlinear Approximation (LEDSNA). Given an
instance being explained, LEDSNA enhances interpretability by feature sampling
with intrinsic dependency. Besides, LEDSNA improves the local explanation
fidelity by approximating nonlinear boundary of local decision. We evaluate our
method with classification tasks in both image domain and text domain.
Experiments show that LEDSNA's explanation of the back-box model achieves much
better performance than original LIME in terms of interpretability and
fidelity.
