that support decision-making based on individual profiling, such as credit and insurance assessment platforms, should ensure that they do not produce discriminatory results by using variables coding for race or ethnicity, income, or any other sensitive information. That means systems must make clear not only what information they are using but also what correlations algorithms are extracting from data for making predictions. The second point is directly related to the systems’ ability to provide (post-hoc) explanations and justify decisions reached after algorithmic assessment in a human-understandable way.

Regardless of the chosen technique to render a model interpretable, the most important aspect to be kept in mind is that interpretability is a human-centered feature. Doshi-Velez and Kim [-@doshi2017roadmap] define interpretability as “the ability to explain or to present in understandable terms to a human”, while Alonso et al. [-@alonso2015interpretability] observe that “the importance of the human component implicitly suggests a novel aspect to be taken into account in the quest for interpretability”, both emphasizing that the aspects of human cognition should be put at the center of modeling decisions. Offering an interpretation of a model can be seen as a knowledge extraction process, and as such it must take into account 