A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rules

In 1959, Arthur Samuel, a pioneer in the field of Artificial Intelligence defined the term Machine Learning [1] as the "field of study that gives computers the ability to learn without being explicitly programmed". In the field of Machine Learning, an important technique called Deep Learning allows "computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction" [2]. In recent years, many accurate decision support systems based on Deep Learning have been constructed as black boxes [3], that is as systems that hide their internal logic to the user. Thus, the purpose of an Explainable Artificial Intelligence [4-7] system is to make its behavior more intelligible to humans by providing explanations [8]. A popular approach to addressing the problem of opacity of black-box machine learning models is the use of post-hoc explainability methods: these methods approximate the logic of underlying machine learning models with the aim of explaining their internal workings, so that the user can understand them [9]. Unfortunately, these methods provide explanations that are not faithful to what the black-box model computes and can be misleading [10]. A recent and highly cited perspective [10] highlighted the need for white box models (i.e.