Ante-Hoc Methods for Interpretable Deep Models: A Survey

Abstract

The increasing use of black-box networks in high-risk contexts has led researchers to propose explainable methods to make these networks transparent. Most methods that allow us to understand the behavior of Deep Neural Networks (DNNs) are post-hoc approaches, implying that the explainability is questionable, as these methods do not clarify the internal behavior of a model. Thus, this demonstrates the difficulty of interpreting the internal behavior of deep models. This systematic literature review collects the ante-hoc methods that provide an understanding of the internal mechanisms of deep models and which can be helpful to researchers who need to use interpretability methods to clarify DNNs. This work provides the definitions of strong interpretability and weak interpretability, which will be used to describe the interpretability of the methods discussed in this paper. The results of this work are divided mainly into prototype-based methods, concept-based methods, and other interpretability methods for deep models.