Science

Prison data analysis finds choosing lowest reoffending risk scores preserves overall accuracy

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 10/11/2026, 23:10:24 UTC.

Briefings

A 2026 analysis by Ashwin Singh and Carlos Castillo suggests that when multiple machine learning models predict recidivism risk, selecting the lowest score improves overall accuracy. This recommendation applies to both human experts and computational models assessing an individual's likelihood of reoffending after prison release.

Key points

• Researchers Ashwin Singh and Carlos Castillo recommend using the lowest risk score among equally reliable models for recidivism assessment.
• The recommendation applies to both human experts and machine learning models evaluating recidivism risk.

Context

Recidivism depends on multiple factors including human and machine-based assessments. The study proposes a specific rule for selecting the most accurate risk rating when multiple models disagree.

Limitations

Evidence is limited to a single study from 2026. No data on model reliability, sample sizes, or real-world application outcomes are provided. The recommendation assumes all models are equally reliable, which may not hold in practice.

Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 10/11/2026, 23:31:37 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.

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