Topological Interpretability for Deep-Learning

arXiv:2305.08642v2 Announce Type: replace-cross
Abstract: With the growing adoption of AI-based systems across everyday life, the need to understand their decision-making mechanisms is correspondingly increasing. The level at which we can trust the statistical inferences made from AI-based decision systems is an increasing concern, especially in high-risk systems such as criminal justice or medical diagnosis, where incorrect inferences may have tragic consequences. Despite their successes in providing solutions to problems involving real-world data, deep learning (DL) models cannot quantify the …

abstract adoption arxiv cs.lg decision diagnosis inferences interpretability justice life making medical risk statistical stat.ml systems trust type