as an inherent attribute of the model. Kotsiantis [-@kotsiantis2007supervised], for example, states that logic-based algorithms such as Naïve Bayes, decision trees, and rule learners are naturally easy to interpret, while neural networks, SVMs and K-NNs have very poor interpretability. Nevertheless, this is not always accepted as a fact. Lipton [-@lipton2016mythos] questions this assumption arguing that “neither linear models, rule-based systems, nor decision trees are intrinsically interpretable”, adding that “sufficiently high-dimensional models, unwieldy rule lists, and deep decision trees could all be considered less transparent than comparatively compact neural networks”. This suggests that interpretability is a property that must be pursued rather than being taken for granted as a result of the model choice.

But what could, in fact, be called *interpretability*? Being tackled in the scope of different approaches, it is natural that the definition of “interpretable model” is not yet something uniformly agreed upon. Lipton [-@lipton2016mythos] observes that “both the motives for interpretability and the technical descriptions of interpretable models are diverse and occasionally discordant, suggesting that interpretability refers to more than one concept”. He argues that the purpose of an interpretation is to convey useful information and that this can be done even without shedding light on a 