aiqbench analysis
This paper clarifies the distinction between structural transparency and scientific content within machine learning models applied to physics, framing interpretability as a deliberate design choice rather than an inherent trait. It outlines specific trade-offs regarding expressivity versus adaptability while highlighting the necessity of rigorous task specification in high-stakes physical sciences. The work provides essential guidance for researchers aiming to integrate explainable AI into fundamental scientific discovery without sacrificing model utility.
Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics
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; 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, differing only in scale, and that interpretability and explainability are best understood as deliberate modeling choices rather than inherent properties. We also emphasize the importance of task specification and intervention plans as a core aspect of model design.
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; 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, differing only in scale, and that interpretability and explainability are best understood as deliberate modeling choices rather than inherent properties. We also emphasize the importance of task specification and intervention plans as a core aspect of model design.
Data Analysis, Statistics and Probability (physics.data-an); Astrophysics of Galaxies (astro-ph.GA); Machine Learning (cs.LG); High Energy Physics - Phenomenology (hep-ph)
arXiv:2606.26228 [physics.data-an]
(or arXiv:2606.26228v2 [physics.data-an] for this version)
Full text under the publisher's open licence · 164 words
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