2026-07-24T03:33:10Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:17595 2024-03-06T11:00:00Z ddc:004 open_access

Decision-Making Under Miscalibration Rothblum, Guy N. Yona, Gal risk prediction calibration algorithmic fairness multi-group fairness How should we use ML-based predictions (e.g., risk of heart attack) to inform downstream binary classification decisions (e.g., undergoing a medical procedure)? When the risk estimates are perfectly calibrated, the answer is well understood: a classification problem’s cost structure induces an optimal treatment threshold j^⋆. In practice, however, predictors are often miscalibrated, and this can lead to harmful decisions. This raises a fundamental question: how should one use potentially miscalibrated predictions to inform binary decisions? In this work, we study this question from the perspective of algorithmic fairness. Specifically, we focus on the impact of decisions on protected demographic subgroups, when we are only given a bound on the predictor’s anticipated degree of subgroup-miscalibration. We formalize a natural (distribution-free) solution concept for translating predictions into decisions: given anticipated miscalibration of α, we propose using the threshold j that minimizes the worst-case regret over all α-miscalibrated predictors, where the regret is the difference in clinical utility between using the threshold in question and using the optimal threshold in hindsight. We provide closed form expressions for j when miscalibration is measured using both expected and maximum calibration error which reveal that it indeed differs from j^⋆ (the optimal threshold under perfect calibration). Schloss Dagstuhl – Leibniz-Zentrum für Informatik Guy N. Rothblum and Gal Yona 2023 Is Part Of LIPIcs, Volume 251, 14th Innovations in Theoretical Computer Science Conference (ITCS 2023) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.ITCS.2023.92 urn:nbn:de:0030-drops-175951 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2023.92 eng https://creativecommons.org/licenses/by/4.0/legalcode