Abstract: In recent years, artificial intelligence(AI) models have become routine in geoscience applications ranging from earthquake early warningsystems to landslide susceptibility mapping, subsurface resource modeling, and stratigraphic classification. Out performing traditional methods for predictiveaccuracy, these models increasingly mediate between observation and inference. However, this technical success raises a critical question: does high predictive accuracy reflect true geological understanding` Thispaper draws attention to the risk that the growing predictive capacity of data-driven models may overshadow the interpretive nature of geoscience andthe elucidation of actual geological conditions. Indomains where observations are sparse, uncertainty is structural, and ground truth is limited—such assubsurface interpretation and hazard assessment—models can achieve seemingly high classification accuracy by relying on mechanistically irrelevant proxies. This undermines model transferability andgeological consistency under changing environmental / tectonic conditions. Without rejecting predictivemodeling, this perspective aims to propose a conceptual and semi-formalized "assistant" framework— not introducing a new algorithm but rather a guidingstructure—that integrates geological constraints, interpretable modeling, and post-hoc geological validation to ensure that predictive performance doesnot override geological reasoning.