the human cognitive factor it inherently involves [@vellido2012making].

Some studies have tried to draw the users’ preferences when it comes to interpretation, especially explanations. Miller et al. [-@miller2017explainable] summarize the findings pointing that people usually judge explanations based on *pragmatic influences* of causes, which include usefulness and relevance, among others, rather than the probability that the cited cause is actually true. Also, people prefer explanations that are *simpler* (cite few causes), more *general* (they explain more events), and *coherent* (consistent with prior knowledge), favoring simpler explanations over more likely explanations. They conclude that “giving simpler explanations that increase the likelihood that the observer both *understands* and *accepts* the explanation may be more useful to establish trust, if this is the primary goal of the explanation” [@miller2017explainable].

As important as defining what interpretability *is* is understanding what *it is for*. Models, and consequently the systems they support, are ultimately designed to address some human user need, and making them interpretable is intended to ensure that the users’ requirements are met in the sense that they can use, understand and trust the system in the simplest and most effective way possible.

Interpretability-driven AI Models
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In this Section, we review different types 