From attribution maps to human-understandable explanations through Concept Relevance Propagation

Abstract The field of explainable artificial intelligence (XAI) aims to bring transparency to today’s powerful but opaque deep learning models. While local XAI methods explain individual predictions in the form of attribution maps, thereby identifying ‘where’ important features occur (but not provid... View More