Towards Fully Interpretable Deep Neural Networks: Are We There Yet?

TL;DR

This paper reviews methods for developing inherently interpretable deep neural networks, especially CNNs, highlighting progress, gaps, and future research directions towards achieving fully transparent AI systems.

Contribution

It provides a comprehensive review of intrinsically interpretable DNNs, focusing on CNNs, and discusses current progress, gaps, and future research directions.

Findings

Current interpretability methods are limited in scope.

Significant gaps remain in achieving full transparency.

Future research should focus on developing inherently interpretable models.

Abstract

Despite the remarkable performance, Deep Neural Networks (DNNs) behave as black-boxes hindering user trust in Artificial Intelligence (AI) systems. Research on opening black-box DNN can be broadly categorized into post-hoc methods and inherently interpretable DNNs. While many surveys have been conducted on post-hoc interpretation methods, little effort is devoted to inherently interpretable DNNs. This paper provides a review of existing methods to develop DNNs with intrinsic interpretability, with a focus on Convolutional Neural Networks (CNNs). The aim is to understand the current progress towards fully interpretable DNNs that can cater to different interpretation requirements. Finally, we identify gaps in current work and suggest potential research directions.