Do latent variables learned by deep models represent real-world sources or artifacts?

Determine whether hidden latent variables learned by deep machine learning models contain information about the real-world source generating the data or whether the internal-state representations are artifacts of the training and model architecture.

Background

The review highlights opacity challenges specific to ML models in scientific contexts, notably whether learned latent representations correspond to genuine features of the physical systems under paper or are merely artifacts of model training.

Resolving this uncertainty is critical for trusting ML-derived scientific insights; if latent variables do reflect real sources, they can serve as interpretable, physically meaningful descriptors. If they are artifacts, reliance on them may mislead scientific inference.