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.
@article{arxiv.2106.13164,
title = {Towards Fully Interpretable Deep Neural Networks: Are We There Yet?},
author = {Sandareka Wickramanayake and Wynne Hsu and Mong Li Lee},
journal= {arXiv preprint arXiv:2106.13164},
year = {2021}
}
Comments
Presented at the ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI