Model Multiplicity and Predictive Arbitrariness in Recidivism Risk Assessment
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 support high-stakes risk assessment? We address these questions through a study of a machine learning-based decision support system for recidivism risk assessment that has been in use for over 15 years. By translating complex legal rules into an algorithm for labeling post release outcomes (recidivist or non-recidivist), we first construct a dataset of tho...
| Authors | Ashwin Singh, Carlos Castillo |
|---|---|
| Primary category | cs.LG |
| Published | 1 June 2026 |
| Last updated at source | 11 August 2026 |
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