BEYOND BLACK BOXES: A FRAMEWORK FOR INHERENT EXPLAINABILITY AND HIGH-ORDER EFFECT ASSESSMENT

Starting from the theoretical study of "Assessing high-order effects in feature importance via predictability decomposition" this thesis implements a scalable approach to decompose feature importance and interactions in Deep Learning models, enabling the efficient analysis of complex dependencies essential for interpreting black-box models.