Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics
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; explainability vs. adaptability), the contexts in which each is needed, and the intrinsic and post-hoc tools available for achieving them. Throughout, we emphasize that machine-learned models are subject to the same scientific questions as classical models, ...
| Authors | Rikab Gambhir, Luisa Lucie-Smith, Jesse Thaler |
|---|---|
| Primary category | physics.data-an |
| Published | 24 June 2026 |
| Last updated at source | 16 August 2026 |
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