Trust, or Don't Predict: Introducing the CWSA Family for Confidence-Aware Model Evaluation
This work addresses the need for reliable model evaluation in safety-critical domains where trust and risk management are essential, though it is incremental as it builds on existing selective prediction frameworks.
The authors tackled the problem of evaluating machine learning models with confidence scores by introducing two new metrics, CWSA and CWSA+, which reward confident accuracy and penalize overconfident mistakes. They demonstrated that these metrics outperform classical ones like accuracy and ECE in detecting failure modes on datasets such as MNIST and CIFAR-10.
In recent machine learning systems, confidence scores are being utilized more and more to manage selective prediction, whereby a model can abstain from making a prediction when it is unconfident. Yet, conventional metrics like accuracy, expected calibration error (ECE), and area under the risk-coverage curve (AURC) do not capture the actual reliability of predictions. These metrics either disregard confidence entirely, dilute valuable localized information through averaging, or neglect to suitably penalize overconfident misclassifications, which can be particularly detrimental in real-world systems. We introduce two new metrics Confidence-Weighted Selective Accuracy (CWSA) and its normalized variant CWSA+ that offer a principled and interpretable way to evaluate predictive models under confidence thresholds. Unlike existing methods, our metrics explicitly reward confident accuracy and penalize overconfident mistakes. They are threshold-local, decomposable, and usable in both evaluation and deployment settings where trust and risk must be quantified. Through exhaustive experiments on both real-world data sets (MNIST, CIFAR-10) and artificial model variants (calibrated, overconfident, underconfident, random, perfect), we show that CWSA and CWSA+ both effectively detect nuanced failure modes and outperform classical metrics in trust-sensitive tests. Our results confirm that CWSA is a sound basis for developing and assessing selective prediction systems for safety-critical domains.