<ul><li>Algorithmic pre-trial risk assessments in the US criminal justice system provide deterministic classification scores and recommendations to help judges in release decisions.</li><li>A research study analyzes data from a field experiment on algorithmic pre-trial risk assessments to investigate the possibility of improving the scores and recommendations.</li><li>Using a maximin robust optimization approach, the study aims to find a policy that maximizes the worst-case expected utility, ensuring the statistical safety of policy improvement.</li><li>The analysis of the field experiment data shows certain components of the risk assessment instrument can be safely improved by classifying arrestees as lower risk under various utility specifications.</li></ul>

Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment

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