Detain/Release: Simulating Algorithmic Risk Assessments at Pretrial

Keith Porcaro, Adjunct Professor, Criminal Justice Technology, Policy, & Law

To date, the debate surrounding pretrial algorithmic risk assessment tools has focused on statistical quality and overall legality. In 2017, when Jason Tasheaand I first taught our Georgetown Law students about risk assessments, our lesson summarized those debates in a lecture and discussion. Afterwards, we worried that students were taking away a simple, wrong lesson: if a software tool is sufficiently “accurate”, it will solve the problem presented and it does not require further investigation.

The discussion shouldn’t end there. Software has framing power: the mere presence of a risk assessment tool can reframe a judge’s decision-making process and induce new biases, regardless of the tool’s quality. This past fall, we wanted our students to engage with this broader, ecosystem-level issue, and to understand the far-reaching consequences that pretrial detention can have on defendants.

This reflects the overall goals of our practicum class, Criminal Justice Technology, Policy, and Law. We want our students to not just be familiar with technology, but be able to think critically about how software and data-driven tools can influence legal ecosystems — sometimes in unexpected ways. After all, law students aren’t training to be programmers or data scientists: they’re training to be advocates. Legal education needs to equip law students to analyze novel technologies in context, and build arguments for or against them.

So, we built a simulation. We call it Detain/Release.

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