Comment by reexpressionist

A major limitation of directly using "System 1 decision models" (a.k.a., logistic regression, and related uncalibrated classifiers over the output/logit space) in enterprise settings (or other high-stakes settings) is that such estimators are not reliable estimators of the predictive uncertainty in the presence of covariate shifts, and such estimators also lack a means of instance-wise data attribution (i.e., interpretability-by-exemplar), so they're not the ideal estimator for verification, routing, uncertainty over retrieval and tool-calls, etc.

For decision-making with neural networks, we instead need the older idea of estimators of the predictive uncertainty with constraints in the feature-representation space (over training/support of the estimator), as with Similarity-Distance-Magnitude estimators: https://pypi.org/project/reexpress-sdm/