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Measuring consistency via ensemble margin and local prediction variability: Auditing decision systems in the presence of predictive multiplicity

arXiv · AI, language, vision and robotics · article · Sep 1, 2026 · UTC

The Rashomon effect is a machine learning phenomenon where equally accurate models produce different predictions for the same inputs (predictive multiplicity). Existing work primarily focuses on multiplicity within individual models, but in more complex decision systems, the impact of the Rashomon effect is less well understood. In this work, we study multiplicity from the perspective of auditing incorrect ensemble predictions, where the decision to divert an instance for human review is based on a consistency criterion that combines the ensemble margin with a measure of local prediction varia

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