From routine security checks at airports to the use of risk assessment in sentencing, actuarial methods are being employed more than ever to determine whom law enforcement officials target and punish. And with the exception of racial profiling on our highways and streets, most people favor these methods because they believe they’re a more cost-effective way to fight crime.
In Against Prediction, Bernard E. Harcourt challenges this growing reliance on actuarial methods. These prediction tools, he demonstrates, may in fact increase the overall amount of crime in society, depending on the relative responsiveness of the profiled populations to heightened security. They may also aggravate the difficulties that profiled persons already have obtaining work, education, and a better quality of life—thus perpetuating the pattern of criminal behavior. Ultimately, Harcourt shows how the perceived success of actuarial methods has begun to distort our very conception of just punishment and to obscure alternative visions of social order. In place of the actuarial, he proposes instead a turn to randomization in punishment and policing. The presumption, Harcourt concludes, should be against prediction.
"[These] arguments should be studied by anyone who is considering advocating, or utilizing, formal predictive methods in the domain of law enforcement."