Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics

Excerpt

arXiv:2606.26228v2 Announce Type: replace-cross Abstract: We review the concepts of interpretability and explainability as they apply to machine learning in physics. We define interpretability as concerning the structural transparency of a model (the ability to understand or approximate its inner workings) and explainability as concerning the scientific content of a model (the ability to map it onto domain knowledge). We discuss the trade-offs each entails (interpretability vs. expressivity; exp