Mitigating Bias in Calibration Error Estimation

TL;DR

This paper investigates statistical bias in calibration error estimation for AI systems, proposing improved estimators that enhance calibration assessment and model reliability.

Contribution

It introduces a framework for bias assessment, identifies better estimators like ECE_sweep, and demonstrates their effectiveness in calibration evaluation.

Findings

Equal-mass binning reduces bias compared to equal-width binning.

The proposed ECE_sweep estimator improves calibration detection.

Debiased estimator and ECE_sweep outperform traditional methods.

Abstract

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: the debiased estimator (Brocker, 2012; Ferro and Fricker, 2012) and a method we propose, ECE_sweep, which uses equal-mass bins and chooses the number of bins to be as large as possible while preserving monotonicity in the calibration function. With these estimators, we observe improvements in the effectiveness of recalibration methods and in the detection of model miscalibration.