2026-07-23T19:58:25Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:25335 2026-01-23T07:22:40Z ddc:004 open_access

Auditability and the Landscape of Distance to Multicalibration Derhake, Nathan Devic, Siddartha Hansen, Dutch Liu, Kuan Sharan, Vatsal Multicalibration Auditability Fairness Classification Calibration Calibration is a critical property for establishing the trustworthiness of predictors that provide uncertainty estimates. Multicalibration is a strengthening of calibration which requires that predictors be calibrated on a potentially overlapping collection of subsets of the domain. As multicalibration grows in popularity with practitioners, an essential question is: how do we measure how multicalibrated a predictor is? Błasiok et al. [Błasiok et al., 2023] considered this question for standard calibration by introducing the distance to calibration framework (dCE) to understand how calibration metrics relate to each other and the ground truth. Building on the dCE framework, we consider the auditability of the distance to multicalibration of a predictor f. We begin by considering what are perhaps the two most natural generalizations of dCE to multiple subgroups: worst group dCE (wdMC), and distance to multicalibration (dMC). Using wdMC and dMC as a guiding path, we argue that there are two essential properties of any multicalibration error metric: 1) the metric should capture how much f would need to be modified in order to be perfectly multicalibrated; and 2) the metric should be auditable in an information theoretic sense (i.e., with some finite sample complexity). We show that wdMC and dMC each fail to satisfy one of these two properties, and that similar barriers arise when considering the auditability of general distance to multigroup fairness notions (e.g. multiaccuracy or low-degree multicalibration). We then propose two (equivalent) multicalibration metrics which do satisfy these requirements: 1) a continuized variant of dMC; and 2) a distance to intersection multicalibration, which leans on intersectional fairness desiderata. Along the way, we shed light on the loss-landscape of distance to multicalibration and the geometry of the set of perfectly multicalibrated predictors. We also demonstrate that the loss surface of any metric which captures how much f would need to be modified to be perfectly multicalibrated often satisfies a local minima are global minima property. Our findings may have implications for the development of stronger multicalibration algorithms, as well as multicalibration auditing more generally. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Nathan Derhake and Siddartha Devic and Dutch Hansen and Kuan Liu and Vatsal Sharan 2026 Is Part Of LIPIcs, Volume 362, 17th Innovations in Theoretical Computer Science Conference (ITCS 2026) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.ITCS.2026.48 urn:nbn:de:0030-drops-253351 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2026.48 eng https://creativecommons.org/licenses/by/4.0/legalcode