The New Science of Sentencing

Thanks to Professor Douglas Berman and his Sentencing Law & Policy Blog, we are alerted to this new Marshall Project feature story about a modern risk assessment tool being used at sentencing.  The piece, carrying the main headline “The New Science of Sentencing,” merits a read in full, and here are excerpts:

Pennsylvania is on the verge of becoming one of the first states in the country to base criminal sentences not only on what crimes people have been convicted of, but also on whether they are deemed likely to commit additional crimes.  As early as next year, judges there could receive statistically derived tools known as risk assessments to help them decide how much prison time — if any — to assign.

Risk assessments have existed in various forms for a century, but over the past two decades, they have spread through the American justice system, driven by advances in social science.  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.  Many court systems use the tools to guide decisions about which prisoners to release on parole, for example, and risk assessments are becoming increasingly popular as a way to help set bail for inmates awaiting trial.

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….

[T]he 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. In theory, using risk assessment tools could lead to both less incarceration and less crime.

There are more than 60 risk assessment tools in use across the U.S., and they vary widely. But in their simplest form, they are questionnaires — typically filled out by a jail staff member, probation officer or psychologist — that assign points to offenders based on anything from demographic factors to family background to criminal history. The resulting scores are based on statistical probabilities derived from previous offenders’ behavior. A low score designates an offender as “low risk” and could result in lower bail, less prison time or less restrictive probation or parole terms; a high score can lead to tougher sentences or tighter monitoring.

The risk assessment trend is controversial. Critics have raised numerous questions: Is it fair to make decisions in an individual case based on what similar offenders have done in the past? Is it acceptable to use characteristics that might be associated with race or socioeconomic status, such as the criminal record of a person’s parents? And even if states can resolve such philosophical questions, there are also practical ones: What to do about unreliable data? Which of the many available tools — some of them licensed by for-profit companies — should policymakers choose?…

The core questions around risk assessment aren’t about data.  They are about what the goals of criminal justice reforms should be.  Some supporters see reducing incarceration as the primary goal; others want to focus on reducing recidivism; still others want to eliminate racial disparities.  Risk assessments have drawn widespread support in part because, as long as they remain in the realm of the theoretical, they can accomplish all those goals.  But once they enter the real world, there are usually trade-offs.

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