necessary when the problem formalization is incomplete, due to, for example, the infeasibility of predicting all possible outputs given all possible inputs, or the abstract nature of some system requirements, such as *fairness* and *trust* [@doshi2017roadmap]. In an incompleteness scenario, explanations are a resource to make possibly flawed results (and their causes) clearly visible, allowing users to act on them.

Biran and Cotton [-@biran2017explanation], focusing on machine learning, also distinguishes between two research branches, which they call *interpretable models*, equivalent to the transparent models in Lipton’s classification; and *prediction interpretation and justification*, that is, the previously seen post-hoc explanations. However, they limit the usage of the term *interpretability* only to the understandability of the model’s internal operations, arguing that a system can provide justifications (as the output) without being interpretable, that is, without making clear to the user what its internal procedures are.

The importance of both varieties of interpretability can be noted in the European Union’s new General Data Protection Regulation, to take effect as law across the EU in 2018. It covers two points tightly related to interpretability: the *non-discrimination* in automated individual decision-making and the *right to explanation* [@goodman2016eu]. The first point refers to algorithmic transparency: systems 