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

Understand

Algorithmic recommendations and decisions have become ubiquitous in today's society.

• Many of these data-driven policies, especially in the realm of public policy, are based on known, deterministic rules to ensure their transparency and interpretability.
• We examine a particular case of algorithmic pre-trial risk assessments in the US criminal justice system, which provide deterministic classification scores and recommendations to help judges make release decisions.
• Our goal is to analyze data from a unique field experiment on an algorithmic pre-trial risk assessment to investigate whether the scores and recommendations can be improved.