Figure 1: Schematic view of the proposed approach, ML-PIE. In the implementation proposed in this paper, the user provides feedback on models that are being discovered by an evolutionary algorithm. This feedback is used to train an estimator which, in turn, shapes one of the objective functions used by the evolution. Ultimately, this steers the evolution towards discovering models that are interpretable according to the specific user. To minimize the amount of feedback needed, ML-PIE keeps track of which models cause the estimator to be most uncertain, and submits these models for user assessment.