Interpretability of Machine Learning Models and Representations: an Introduction

Interpretability is often a major concern in machine learning. Although many authors agree with this statement, interpretability is often tackled with intuitive arguments, distinct (yet related) terms and heuristic quantifications. This short survey aims to clarify the concepts related to interpretability and emphasises the distinction between interpreting models and representations, as well as heuristic-based and user-based approaches.