Mitigating Bias in Calibration Error Estimation
This work is significant for researchers and practitioners in AI reliability, as it provides more accurate methods for assessing model calibration, which is crucial for trustworthy AI systems. It is an incremental improvement in the methodology of calibration error estimation.
This paper addresses statistical bias in empirical calibration error estimation, a critical aspect for reliable AI systems where confidence should match accuracy. The authors propose a framework to compute estimator bias and identify that equal-mass binning estimators have lower bias than equal-width binning. They recommend the debiased estimator and their new ECE_sweep method, which uses equal-mass bins and maximizes bin count while maintaining monotonicity, leading to improved recalibration and miscalibration detection.
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.