Mitigating the Multiplicity Burden: The Role of Calibration in Reducing Predictive Multiplicity of Classifiers

ArXi:2603.11750v1 Announce Type: new As machine learning models are increasingly deployed in high-stakes environments, ensuring both probabilistic reliability and prediction stability has become critical. This paper examines the interplay between classification calibration and predictive multiplicity - the phenomenon in which multiple near-optimal models within the Rashomon set yield conflicting credit outcomes for the same applicant.