need for evidence to support decision making but also on the demand to easily identify biased correlations that could go unnoticed otherwise. Zhao et al. [-@zhao2017men] argues that many prediction models risk reflecting social biases found in data, showing that, using an image dataset containing significant gender bias where the activity “cooking” was over 33% more likely to involve women than men, a trained model further amplifies the disparity to 68% at test time. Transported to the decision-making scenario, sensitive information such as gender, race, religion or income, for example, can lead to unfair predictions on tasks that involve individual profiling, such as hiring, loan granting or crime prediction, to name a few, where certain groups can be subject to discrimination. Even though the model’s predictions may seem to conform to previous decisions, if those decisions were influenced by social biases and this is reflected in the data they generated, an interpretable model can make that clear and allow for more fairness on future verdicts.

Although semantic interpretability is gaining renewed attention due to the increasing use of machine learning, being interpretable is not an issue exclusive to these models, but a requirement for any approach dealing with AI. For 