Testable and Actionable Calibration for Full Swap Regret

TL;DR

This paper introduces SCDL, a new calibration measure for AI predictions that is both fully actionable and testable, addressing key limitations of existing measures.

Contribution

The paper proposes SCDL, a calibration measure that is simultaneously actionable and testable, with proven theoretical properties and empirical validation.

Findings

SCDL is fully actionable without weakening calibration requirements.

SCDL can be tested with nearly optimal estimation error.

Experiments show SCDL outperforms existing calibration measures in practice.

Abstract

AI generated predictions increasingly inform decision making in critical tasks, and therefore must be trustworthy. One widely used measure of trustworthiness is calibration, which requires that the predictions match the true frequencies and can be treated like real probabilities of a given outcome. However, defining calibration is subtle, and designing good measures of calibration error has been an active topic of recent research. The first goal is to find calibration measures that are actionable, meaning they can inform decision makers about their utility loss when predictions are treated as true probabilities, which is known as swap regret. The second goal is to find calibration measures that are testable, meaning that calibration error can be measured from a small sample of predictions and outcomes. Although these are very basic requirements, there is no existing calibration measure that fully satisfies both properties, and all existing measures relax actionability by bounding a weaker notion of swap regret, or relax testability by having suboptimal estimation error. We introduce a new calibration measure, Soft-Binned Calibration Decision Loss (SCDL), which we prove is fully actionable without weakening either requirement, and testable with nearly optimal error rate. In addition, SCDL satisfies other desired properties such as continuity and consistency. We also provide a set of experiments confirming that the theoretical advantages of SCDL compared to other measures lead to better performance in practice.