Opening the Black Box: Empowering Machine Learning Models with Explanations

The thesis tackles two problems in the recently-born field of Explainable AI (XAI), and proposes some algorithms to solve them. XAI has the overarching goal of providing human-understandable explanations of Machine Learning models, which, nowadays, operate as highly complex black-box models whose decisions, especially in high-stakes and critical settings, we are not able to understand. The thesis tackles the novel problem of Local-to-Global (L2G) explainability, and local explainability. In a L2G setting one wishes to infer an understanding of the overall behavior of a model starting from explanations of its punctual decisions, that is, to infer global explanations from local ones. We propose two Local-to-Global algorithms to tackle this problem, Rule Relevance Score and GLocalX. Then, we focus on local explainability, and provide an algorithm, TriplEx, to explain Transformer-based models on a variety of tasks.