Ricerc@Sapienza

In the past two decades, AI systems have achieved an unprecedented level of efficiency and are being deployed to help us understand and comprehend the challenges we face. Yet, the adoption of modern AI systems in many domains today is limited by the low interpretability of machine predictions and especially deep neural networks. Current approaches to make DNNs outputs interpretable are focused on weak forms of explainability which are not satisfactory in many fields. With this project, we want to investigate the explainability potential of Rationalist Learning, a new machine learning paradigm used for theoretical inductive problems, and that we want to test on real-world problems such as process mining and object detection in computer vision. Such an approach would guarantee strong explainability and could unlock deep learning-based AI in many fields.