ALIME: Autoencoder Based Approach for Local Interpretability

TL;DR

This paper introduces ALIME, an autoencoder-enhanced local interpretability method that improves explanation stability and fidelity for deep learning models, especially in sensitive domains like medicine.

Contribution

The paper proposes a novel modification to LIME using autoencoders to enhance explanation stability and local fidelity in model interpretability.

Findings

ALIME outperforms LIME in stability of explanations.

ALIME achieves higher local fidelity across datasets.

Autoencoder integration improves interpretability reliability.

Abstract

Machine learning and especially deep learning have garneredtremendous popularity in recent years due to their increased performanceover other methods. The availability of large amount of data has aidedin the progress of deep learning. Nevertheless, deep learning models areopaque and often seen as black boxes. Thus, there is an inherent need tomake the models interpretable, especially so in the medical domain. Inthis work, we propose a locally interpretable method, which is inspiredby one of the recent tools that has gained a lot of interest, called localinterpretable model-agnostic explanations (LIME). LIME generates singleinstance level explanation by artificially generating a dataset aroundthe instance (by randomly sampling and using perturbations) and thentraining a local linear interpretable model. One of the major issues inLIME is the instability in the generated explanation, which is caused dueto the randomly generated dataset. Another issue in these kind of localinterpretable models is the local fidelity. We propose novel modificationsto LIME by employing an autoencoder, which serves as a better weightingfunction for the local model. We perform extensive comparisons withdifferent datasets and show that our proposed method results in bothimproved stability, as well as local fidelity.