A unified framework for global and local interpretability using adaptive derivative-ordered random explanation.

arXiv:2609.17171v2 Announce Type: replace-cross Abstract: The interpretability of complex machine learning models is of paramount importance, especially in real-world high-stakes domains such as healthcare and finance. However, existing post-hoc interpretabili