Interpretability in Machine Learning: on the Interplay with
  Explainability, Predictive Performances and Models
Benjamin Leblanc Pascal Germain
Interpretability has recently gained attention in the field of machine
learning, for it is crucial when it comes to high-stakes decisions or
troubleshooting. This abstract concept is hard to grasp and has been
associated, over time, with many labels and preconceived ideas. In this
position paper, in order to clarify some misunderstandings regarding
interpretability, we discuss its relationship with significant concepts in
machine learning: explainability, predictive performances, and machine learning
models. For instance, we challenge the idea that interpretability and
explainability are substitutes to one another, or that a fixed degree of
interpretability can be associated with a given machine learning model.