model’s inner workings. This leads to the division of interpretability techniques into two broad categories: one referring to *transparency*, asking *how the model works*, and the other relating to *post-hoc explanations*, inquiring *what else the model can tell* [@lipton2016mythos]. That means that interpretability can be related to the system output, but also to the system architecture itself.

Most of the models that claim to be interpretable focus on *transparency*, that is, given the input data and the model parameters, it should be possible to step through the calculations that lead to a prediction. Transparency refers not only to the model as a whole (*simulatability*), but also to each of its parts: each input, parameter, and calculation should admit an intuitive explanation (*decomposability*) [@lipton2016mythos].

*Post-hoc explanations*, on the other hand, can be seen as an abstraction layer for the model, as they can summarize and translate the system’s procedures into a friendlier format, exempting the user from going through algorithmic details. Natural language explanations, visualizations of learned representations or models, and explanations by example (such as presenting the k-nearest neighbors of a word given its vector representation) are some common semiotic approaches for providing post-hoc model interpretation [@lipton2016mythos]. Explanations are especially 