Interpretability of deep neural networks: A review of methods, classification and hardware

Abstract

Artificial intelligence, and especially deep neural networks, have evolved substantially in the recent years, infiltrating numerous domains of applications, often greatly impactful to society’s well-being. As a result, the need to understand how these models operate in depth and to access explanations of their decisions has become more vital than ever. Tending to this demand, the following paper aims to provide a thorough overview of the methods that have so far been developed to explain deep neural networks. Key aspects of explainability are defined and a straightforward classification of existing approaches is introduced, along with numerous examples. The task of realizing these methods on hardware is also discussed to complete the understanding of their application.