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Calibration is a popular framework to evaluate whether a classifier knowswhen it does not know - i.e., its predictive probabilities are a goodindication of how likely a prediction is to be correct. Correctness is commonlyestimated against the human majority class. Recently, calibration to humanmajority has been measured on tasks where humans inherently disagree aboutwhich class applies. We show that measuring calibration to human majority giveninherent disagreements is theoretically problematic, demonstrate thisempirically on the ChaosNLI dataset, and derive several instance-level measuresof calibration that capture key statistical properties of human judgements -class frequency, ranking and entropy.

Stop Measuring Calibration When Humans Disagree