LIPEx-Locally Interpretable Probabilistic Explanations-To Look Beyond The True Class

TL;DR

LIPEx is a novel perturbation-based explanation method that accurately replicates model probabilities and reveals feature effects on predictions, outperforming existing methods in data efficiency and explanation quality.

Contribution

This paper introduces LIPEx, a new framework for local probabilistic explanations that improves upon LIME in accuracy and efficiency by using a matrix regression approach based on the Hellinger distance.

Findings

LIPEx replicates model probability distributions more accurately.

LIPEx provides detailed feature impact on class probabilities.

LIPEx is approximately 53% faster than LIME in explanation computation.

Abstract

In this work, we instantiate a novel perturbation-based multi-class explanation framework, LIPEx (Locally Interpretable Probabilistic Explanation). We demonstrate that LIPEx not only locally replicates the probability distributions output by the widely used complex classification models but also provides insight into how every feature deemed to be important affects the prediction probability for each of the possible classes. We achieve this by defining the explanation as a matrix obtained via regression with respect to the Hellinger distance in the space of probability distributions. Ablation tests on text and image data, show that LIPEx-guided removal of important features from the data causes more change in predictions for the underlying model than similar tests based on other saliency-based or feature importance-based Explainable AI (XAI) methods. It is also shown that compared to LIME, LIPEx is more data efficient in terms of using a lesser number of perturbations of the data to obtain a reliable explanation. This data-efficiency is seen to manifest as LIPEx being able to compute its explanation matrix around 53% faster than all-class LIME, for classification experiments with text data.