  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.
