Google Research

Mitigating bias in calibration error estimation

  • Becca Roelofs
  • Nick Cain
  • Jon Shlens
  • Michael Mozer
AIStats (2022) (to appear)


For an AI system to be reliable, the confidence it expresses in its decisions must match its accuracy. To assess the degree of match, examples are typically binned by confidence and the per-bin mean confidence and accuracy are compared. Most research in calibration focuses on techniques to reduce this empirical measure of calibration error, ECE_bin. We instead focus on assessing statistical bias in this empirical measure, and we identify better estimators. We propose a framework through which we can compute the bias of a particular estimator for an evaluation data set of a given size. The framework involves synthesizing model outputs that have the same statistics as common neural architectures on popular data sets. We find that binning-based estimators with bins of equal mass (number of instances) have lower bias than estimators with bins of equal width. Our results indicate two reliable calibration-error estimators: a variant of the debiased estimator of Kumar et al. (2019) using equal mass bins, and a method we propose, ECE_sweep, in which the number of bins is chosen to be as large as possible while preserving monotonicity in the calibration function. With improved estimators, we observe improvements in the effectiveness of recalibration methods and in the detection of model miscalibration.

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