Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthrough
For physicists using ML in particle physics, astrophysics, and cosmology, this paper provides a framework for verifying AI reliability before discovery claims, but it is a review without concrete results.
This review synthesizes the VERaiPHY initiative's frameworks for rigorous ML assessment in fundamental physics, emphasizing that inductive bias, sample complexity, and experimental constraints fundamentally limit AI-driven discovery. It argues that responsible integration requires understanding ML's transformative potential alongside its intrinsic boundaries.
Machine learning (ML) has become integral to fundamental physics, accelerating statistical workflows from data acquisition through inference and hypothesis testing. As ML systems grow increasingly autonomous, ensuring their reliability for discovery claims becomes critical. This review synthesizes the VERaiPHY (Validation & Evaluation for Robust AI in PHYsics) initiative's frameworks for rigorous ML assessment across particle physics, astrophysics, and cosmology. We establish when verification is essential by contextualizing ML within the statistical discovery workflow. We emphasize fundamental limitations: inductive bias is unavoidable, sample complexity bounds learning, and experimental constraints limit discovery. We reflect on physicists' evolving role as both experimental designers and evaluators whose judgments encode scientific rigor into AI systems. Responsible integration requires understanding ML's transformative potential alongside its intrinsic boundaries.