Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics
arXiv:2606.26228v1 Announce Type: 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 av
The paper is published as the field of AI and its application in scientific domains, especially physics, is rapidly expanding, leading to a critical need for rigorous definitions and practices around interpretability and explainability.
Understanding the precise definitions and trade-offs of interpretability and explainability is crucial for developing trustworthy and verifiable AI in scientific discovery and high-stakes fields.
This paper establishes a clearer conceptual framework for evaluating machine learning models in physics, distinguishing between structural transparency (interpretability) and scientific alignment (explainability).
- · AI ethicists
- · Physics researchers using ML
- · Funding bodies for AI research
- · Scientific software developers
- · Developers of 'black box' AI models
- · Skeptics of AI in scientific discovery
Increased focus on model transparency and scientific alignment will influence future AI research methodologies in physics.
New tools and frameworks will emerge to measure and enhance interpretability and explainability in domain-specific AI applications.
The clearer definitions could lead to standardizations or regulatory guidelines for AI deployment in sensitive scientific and engineering fields.
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