Decision-Aligned Evaluation of Uncertainty Quantification

2026-06-25

AI Summary

This work addresses the misalignment between conventional uncertainty quantification metrics—such as negative log-likelihood and expected calibration error—and the utility of downstream decision-making, which often renders them poor proxies for real-world decision value. To bridge this gap, the paper introduces a “decision-aligned” evaluation principle, systematically exposing the mismatch between widely used scoring rules and common decision tasks. Building on decision theory and proper scoring rules, the authors propose a class of prior-weighted utility-based metrics that directly reflect the impact of predictive uncertainty on decision outcomes. Empirical evaluations across multiple benchmarks and real-world scenarios demonstrate that the proposed metric consistently correlates strongly with actual decision utility, significantly outperforming traditional approaches and offering a principled foundation for decision-relevant uncertainty assessment.

Abstract

Uncertainty estimates in machine learning are typically evaluated using generic metrics such as the negative log-likelihood and expected calibration error, yet good performance on such metrics does not necessarily imply high utility in downstream decisions. We introduce decision-alignment, a criterion that reveals which evaluation metrics meaningfully align with downstream utilities. Applying this framework, we show that many widely used uncertainty metrics are either misaligned with common decision problems or encode pathological prior beliefs about the downstream task. We then propose prior-weighted utility metrics, a special class of proper scoring rules that provides decision-aligned uncertainty evaluation. Across benchmark experiments and real-world case studies, our metrics consistently align with realized decision utility, while conventional metrics do not. Our results surface flaws in the current UQ evaluation protocol and offer a principled extension of existing metrics toward decision-relevant UQ evaluation.

Problem

• Uncertainty Quantification
• Decision Alignment
• Evaluation Metrics
• Downstream Utility
• Proper Scoring Rules

Innovation

• decision-alignment
• uncertainty quantification
• proper scoring rules
• prior-weighted utility
• downstream decision utility