techniques are now being largely used in tasks such as medical diagnosis, insurance and credit assessment, and criminal recidivism prediction, among others. In those cases, even though a system is known to make accurate predictions, explaining and justifying these predictions may be crucial for users to trust it and make further decisions based on these outputs. Same Google has just released a new AI algorithm capable of predicting heart diseases by analyzing data generated from scans of the back of patients’ eyes [@poplin2018prediction]. This algorithm can make the assessment of a patient’s cardiovascular risk quicker and easier, but, although all authors say is that it still needs to be thoroughly further tested before being used by doctors, it also needs to be interpretable: it must make clear what information from the scans and what correlations between them are leading to a diagnosis, so doctors can have the necessary evidence to judge whether to follow the system’s recommendation or not. Moreover, relying on such technology for prescribing medical treatments won’t allow for a quick workaround in case the model start showing undesirable behavior, as in the image classification scenario.

The importance of the so-called Explainable AI lies not only on the 