A Risk Assessment of a Pretrial Risk Assessment Tool: Tussles, Mitigation Strategies, and Inherent Limits

TL;DR

This paper critically evaluates the risks and limitations of the PSA pretrial risk assessment tool, introduces the Handoff Tree algorithm to mitigate inherent issues, and discusses the ethical implications of automating judicial decisions.

Contribution

It presents a comprehensive risk assessment of PSA, introduces the Handoff Tree algorithm to address limitations, and explores ethical considerations in automated pretrial justice.

Findings

Handoff Tree pairs predictions with error rates to improve decision transparency.

Explicit error rates help reduce racial and gender disparities.

The approach encourages judges to critically evaluate risk assessments.

Abstract

We perform a risk assessment of the Public Safety Assessment (PSA), a software used in San Francisco and other jurisdictions to assist judges in deciding whether defendants need to be detained before their trial. With a mixed-methods approach including stakeholder interviews and the use of theoretical frameworks, we lay out the values at play as pretrial justice is automated. After identifying value implications of delegating decision making to technology, we articulate benefits and limitations of the PSA solution, as well as suggest mitigation strategies. We then draft the Handoff Tree, a novel algorithmic approach to pretrial justice that accommodates some of the inherent limitations of risk assessment tools by design. The model pairs every prediction with an associated error rate, and hands off the decision to the judge if the uncertainty is too high. By explicitly stating error rate, the Handoff Tree aims both to limit the impact of predictive disparity between race and gender, and to prompt judges to be more critical of retention recommendations, given the high rate of false positives they often entail.