The neighborhood you live in might control which schools you have access to and what a house costs. But it might also control what kind of sentencing guidelines are in effect if you have the misfortune of being sent to jail.
Pennsylvania will soon begin using factors other than a convict’s criminal history — such as the person’s age, gender, or county of residence — in its sentencing guidelines…
This is part of a long-running discussion on data-driven approaches to recidivism and criminal justice in general. The (honorable) rationale behind these efforts is to eliminate explicit bias/prejudice in sentencing, which (as the article points out) was governed by more lax guidelines than for investigating crimes in the first place.
And as always, I don’t think there’s a problem in using data-driven methods to inform and guide the underlying mechanisms that might cause recidivism. But it gets a lot trickier when these tools are used to guide sentencing. In the case of Pennsylvania, the tool will be used to “assist” judges in sentencing decisions. And as Goodhart’s law suggests, once you attempt to quantify something as nebulous as “likely to re-offend”, the quantification takes on a life of its own.
But even this isn’t the biggest problem. There’s now a long history of models for predicting recidivism, and one of the most studied ones is the LSI-R (and its generalization, the LSRNR). But these models are proprietary: you need to purchase the software to see what they do.
And if you think that’s not a problem, I’d like to point you to this article by Rebecca Weller in Slate. In particular
Today, closed, proprietary software can put you in prison or even on death row. And in most U.S. jurisdictions you still wouldn’t have the right to inspect it.
And as she goes on to point out,
Inspecting the software isn’t just good for defendants, though—disclosing code to defense experts helped the New Jersey Supreme Court confirm the scientific reliability of a breathalyzer.
At the heart of these arguments is a point that has nothing to do with algorithms.
Wanting transparency does not imply lack of trust. It reflects a concern about a potential violation of trust.