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Interpretability has recently gained attention in the field of machinelearning, for it is crucial when it comes to high-stakes decisions ortroubleshooting. This abstract concept is hard to grasp and has beenassociated, over time, with many labels and preconceived ideas. In thisposition paper, in order to clarify some misunderstandings regardinginterpretability, we discuss its relationship with significant concepts inmachine learning: explainability, predictive performances, and machine learningmodels. For instance, we challenge the idea that interpretability andexplainability are substitutes to one another, or that a fixed degree ofinterpretability can be associated with a given machine learning model.

Interpretability in Machine Learning: on the Interplay with Explainability, Predictive Performances and Models