Explaining the black-box or using the black-box to develop better interpretable solutions?

Understanding the decision-making processes behind Artificial Intelligence models became a crucial aspect of AI. This paper describes a study that compares the performance of models produced by both interpretable and black-box algorithms and evaluates if it is possible to use black-box models to assist in interpretable models' training. We verified a significant difference in performance between the two types of models. However, the interpretable model was able to mimic the behavior of the black-box models to a satisfactory degree. The promising initial results obtained from using black-box models to aid in interpretable models' training suggest the potential efficacy of this approach.