Breaking the Paradox of Explainable Deep Learning
Arlind Kadra Sebastian Pineda Arango Josif Grabocka
Deep Learning has achieved tremendous results by pushing the frontier of
automation in diverse domains. Unfortunately, current neural network
architectures are not explainable by design. In this paper, we propose a novel
method that trains deep hypernetworks to generate explainable linear models.
Our models retain the accuracy of black-box deep networks while offering free
lunch explainability by design. Specifically, our explainable approach requires
the same runtime and memory resources as black-box deep models, ensuring
practical feasibility. Through extensive experiments, we demonstrate that our
explainable deep networks are as accurate as state-of-the-art classifiers on
tabular data. On the other hand, we showcase the interpretability of our method
on a recent benchmark by empirically comparing prediction explainers. The
experimental results reveal that our models are not only as accurate as their
black-box deep-learning counterparts but also as interpretable as
state-of-the-art explanation techniques.