no means exhaustive; we sought to pick representative examples of each class, focusing on the ones that emphasize and prioritize interpretability as a driving design choice in the model construction.

Interpretability across Models
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Interpretability issues have been gaining the spotlight in the recent years due to the fast advancements and widespread utilization of machine learning techniques, especially the ones based on deep neural network models. Such models proved to be powerful predictors, but its complexity usually prevents the user from understanding its internal dynamics. To trust supervised machine learning models we need them to be not only accurate, but interpretable [@lipton2016mythos]. However, the need for interpretability is not exclusive to machine learning models. Rule-based models can also grow in size and complexity to a point where users are similarly left unable to comprehend them, also requiring interpretability issues to be taken into account, so that keeping track of those models’ decisions becomes feasible. In fact, interpretability has been a key issue in many different areas of AI for many years, notably in the design of fuzzy logic models, giving origin to a number of theoretical and practical studies regarding this topic [@alonso2011special; @mencar2011interpretability; @alonso2014some].

Being interpretable is sometimes regarded 