an unanticipated consequence of
Jack M. Balkin
Jack Balkin: jackbalkin at yahoo.com
Bruce Ackerman bruce.ackerman at yale.edu
Ian Ayres ian.ayres at yale.edu
Mary Dudziak mary.l.dudziak at emory.edu
Joey Fishkin joey.fishkin at gmail.com
Heather Gerken heather.gerken at yale.edu
Abbe Gluck abbe.gluck at yale.edu
Mark Graber mgraber at law.umaryland.edu
Stephen Griffin sgriffin at tulane.edu
Bernard Harcourt harcourt at uchicago.edu
Scott Horton shorto at law.columbia.edu
Andrew Koppelman akoppelman at law.northwestern.edu
Marty Lederman msl46 at law.georgetown.edu
Sanford Levinson slevinson at law.utexas.edu
David Luban david.luban at gmail.com
Gerard Magliocca gmaglioc at iupui.edu
Jason Mazzone mazzonej at illinois.edu
Linda McClain lmcclain at bu.edu
John Mikhail mikhail at law.georgetown.edu
Frank Pasquale pasquale.frank at gmail.com
Nate Persily npersily at gmail.com
Michael Stokes Paulsen michaelstokespaulsen at gmail.com
Deborah Pearlstein dpearlst at princeton.edu
Rick Pildes rick.pildes at nyu.edu
Richard Primus raprimus at umich.edu
K. Sabeel Rahmansabeel.rahman at brooklaw.edu
Alice Ristroph alice.ristroph at shu.edu
Neil Siegel siegel at law.duke.edu
Brian Tamanaha btamanaha at wulaw.wustl.edu
Mark Tushnet mtushnet at law.harvard.edu
Adam Winkler winkler at ucla.edu
Neyfakh raises some criticisms of risk prediction, and the only thing I would add is that, even if you assume that these prediction tools work, in all likelihood their use undermines the goals that they are intended to promote. We usually adopt these risk prediction instruments with the goal of reducing future crime. The fact is, however, that profiling on risk likely is going to increase overall crime in society, either because (if you believe that people are “rational actors” and change their criminal behavior based on the amount of punishment) the profiled people are less likely to respond much to the increased punishment whereas everyone else will respond much more to the decreased punishment, or because (if you believe that people don’t change their behavior because of the amount of punishment) profiling is going to disproportionately affect the profiled group, which is going to have devastating effects on their employment, family, and social outcomes.
There arguments are a mouthful, I realize, and I spell them out in detail in Against Prediction, but just to be clear, let me give a paragraph to each. First, even if we assume that individuals respond to increased punishment by offending less (even if we adopt the economists’ assumptions of choice), the overall long-term effect is likely to be counter-productive if the profiled population is less responsive to punishment than the non-profiled population. Profiling may well deter crime among the profiled population, but if so, it will likely increase the offending rates of non-profiled groups. If those others are more responsive to changes in punishment, then the overall rate of crime in society will go up. And the truth is, we have no idea about the comparative responsiveness of different groups to punishment, but a reasonable and conservative guess is that, if a population has a higher offending rate, it probably is less elastic to punishment. Most explanations for why a group would have a higher offending rate than other groups would also suggest that the group is less responsive to punishment.
Second, even if people don’t change their behaviors in response to punishment, the problem is that profiling is going to have a disproportionate effect on members of the profiled population and this distortion is only going to get worse with every new annual crime statistic. It may surprise, but the only way to get a prison population that reflects the offending population is to police and punish randomly: to sample randomly. That’s why we use random sampling in the social sciences. Anytime you start picking and choosing, your sample is going to be skewed. And the point is, the skew has devastating consequences for the profiled group, not only on their criminal justice outcomes, but also on their job potential, on their educational outcomes, and on their family lives—which in turn fuels increased criminal activity.
The bottom line is that there is no good law enforcement argument for profiling on risk prediction: the practice is in all likelihood counter-productive to the very goals of law enforcement. The other big danger, of course, is that these risk instruments rely increasingly on prior criminal history and, because of disparate treatment in policing, prior criminal history is increasingly a proxy for race. Therefore, the use of these risk tools is likely going to worsen the already disproportionate effect of the criminal justice system on minorities.
Neyfakh ends his article with an interesting conundrum—the question of where all this will lead us. Neyfakh recounts: “Supposing I am able to tell a mother that her 8-year-old has a one in three chance of committing a homicide by age 18,” says Richard Berk at the University of Pennsylvania. “What the hell do I do with that information? What do the various social services do with that information? I don’t know.”
One answer, I take it, would be to divert more resources toward that youth. That’s exactly what the City of Chicago began to do over the past year, with school officials creating a “risk model” to predict who is more likely to be the victim of gun violence and then to target mentoring and other resources toward them. In other words, to affirmatively target higher-risk youngsters so as to reduce the odds.
As a critic of prediction, that’s the toughest case for me. Is it alright to use risk prediction to target treatment to youth in order to help them beat the odds? I’m afraid the answer, again, is no, because it’s likely that the diversion of resources away from the youths who are less at risk is likely to have a greater and more detrimental effect on them. The best bet is to invest equally in our youths. Equally, and heavily. Posted
by Bernard E. Harcourt [link]