(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Andrea Panizza asks me what I think of this post by Anna Maria Barry-Jester, Ben Castleman, and Dana Goldstein, “Should Prison Sentences Be Based On Crimes That Haven’t Been Committed Yet?” The post begins as follows:
Criminal sentencing has long been based on the present crime and, sometimes, the defendant’s past criminal record. In Pennsylvania, judges could soon consider a new dimension: the future.
Is that really new? I thought that the question of whether someone will re-offend was already considered in sentencing. For example, this bit from Errol Morris: “You can’t predict future violence, you can’t predict human behavior except in one instance: What those two doctors, Dr. Grigson and Dr. [John] Holbrook, would say in the penalty phase of a capital murder trial. They’ll say the defendant will kill again. And as a result, the jury imposes the death penalty.” So I’m not quite sure why Barry-Jester et al. say this is new. Maybe it’s just new for Pennsylvania? (Morris was talking about Texas.)
Anyway, here’s what they write:
As early as next year, judges [in Pennsylvania] could receive statistically derived tools known as risk assessments to help them decide how much prison time — if any — to assign. . . . The tools try to predict recidivism — repeat offending or breaking the rules of probation or parole — using statistical probabilities based on factors such as age, employment history and prior criminal record. They are now used at some stage of the criminal justice process in nearly every state. . . . But Pennsylvania is about to take a step most states have until now resisted for adult defendants: using risk assessment in sentencing itself. A state commission is putting the finishing touches on a plan that, if implemented as expected, could allow some offenders considered low risk to get shorter prison sentences than they would otherwise or avoid incarceration entirely. Those deemed high risk could spend more time behind bars.
I think the whole “behind bars” thing is overdone, but let’s set that aside. Conditional on our current policy (misguided, in my opinion) of prison time as the standard punishment, I don’t see anything wrong with sentences that are based on forecasts.
Barry-Jester et al. quote an opponent of the policy:
Sonja Starr, a University of Michigan law professor who has been a leading opponent of risk assessment, says it isn’t fair. “These instruments aren’t about getting judges to individually analyze life circumstances of a defendant and their particular risk,” she said. “It’s entirely based on statistical generalizations.”
But ultimately the decisions are based on individual information that goes into the forecast, thus “life circumstances,” no? To me, the title, “Should Prison Sentences Be Based On Crimes That Haven’t Been Committed Yet?”, is misleading, as the prison sentences would be based on past information, it’s just information that is statistically correlated with recidivism.
P.S. As an aside, I’m baffled by the political angle of the post. Barry-Jester et al. write:
The approach has bipartisan appeal: Among some conservatives, risk assessment appeals to the desire to spend tax dollars on locking up only those criminals who are truly dangerous to society. And some liberals hope a data-driven justice system will be less punitive overall and correct for the personal, often subconscious biases of police, judges and probation officers.
This baffles me. Liberals too want to spend less tax dollars on prisons, and liberals also want to emphasize locking up criminals who are truly dangerous to society. And conservatives also are concerned about biases of police, judges and probation officers. Bipartisan is fine, but I don’t understand this take on it.
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