Does calibration mean what they say it means; or, the reference class problem rises again
Lily Hu

TL;DR
The paper argues that calibration does not guarantee consistent interpretation of risk scores across groups and highlights the reference class problem as a fundamental issue in fairness criteria.
Contribution
It critically examines the assumption that calibration ensures fairness and reveals the pervasive influence of the reference class problem in fairness metrics.
Findings
Calibration cannot ensure consistent score interpretation across groups
The reference class fallacy undermines fairness claims based on calibration
The reference class problem affects multiple group fairness criteria
Abstract
Discussions of statistical criteria for fairness commonly convey the normative significance of calibration within groups by invoking what risk scores "mean." On the Same Meaning picture, group-calibrated scores "mean the same thing" (on average) across individuals from different groups and accordingly, guard against disparate treatment of individuals based on group membership. My contention is that calibration guarantees no such thing. Since concrete actual people belong to many groups, calibration cannot ensure the kind of consistent score interpretation that the Same Meaning picture implies matters for fairness, unless calibration is met within every group to which an individual belongs. Alas only perfect predictors may meet this bar. The Same Meaning picture thus commits a reference class fallacy by inferring from calibration within some group to the "meaning" or evidential value of…
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Taxonomy
TopicsFault Detection and Control Systems · Flow Measurement and Analysis
