Model Multiplicity and Predictive Arbitrariness in Recidivism Risk Assessment

ArXi:2606.02198v1 Announce Type: new Prediction tasks over individual futures, which are inherently noisy, often admit multiple similarly accurate models. When these models produce different predictions for the same individual, they raise concerns of arbitrariness in decision-making. How severe can this arbitrariness be, in theory and in practice? How can it be resolved to high-stakes risk assessment? We address these questions through a study of a machine learning-based decision system for recidivism risk assessment that has been in use for over 15 years.