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Prediction of Crime and Recidivism - Conclusions And Implications

criminal behavioral data probability

In no sense do the data on the prediction of criminal behavior compel their own policy implications. Given that the level of predictive validity revealed in the research has at least in the case of violent crime been rather modest, one could use the data to argue for across-the-board reductions in the length of institutionalization of prisoners: since society cannot be sure who will do harm, it should detain no one. Alternatively, and with equal fervor and logic, one could use the same data to argue for across-the-board increases in the length of institutionalization: since society cannot be sure which offenders will be nonviolent, it should keep them all in. Whether one uses the data in support of the first or the second of these implications will depend upon how one assesses and weighs the various costs and benefits associated with each, or upon the nonutilitarian principles for punishment that one adopts. In regard to the former approach, the principal impediment to developing straightforward cost-benefit ratios for predictive decision-making is the lack of a common scale along which to order both costs and benefits. For example, how are "years in a prison" to be compared with rapes, robberies, murders, or assaults prevented? John Monahan and David Wexler (p. 38) have argued in this regard that when a behavioral scientist predicts that a person will be "dangerous" to the extent that state intervention is needed, that scientist is making three separable assertions:

  1. The individual being examined has certain characteristics.
  2. These characteristics are associated with a certain probability of violent behavior.
  3. The probability of violent behavior is sufficiently great to justify preventive intervention.

The first two of these assertions, Monahan and Wexler hold, are professional judgments within the expertise of the behavioral sciences—judgments that can, of course, be challenged in court. The third is a social-policy statement that must be arrived at through the political process, and upon which the behavioral scientist should have no more say than any other citizen. What the behavioral scientist should do, they argue, is to present and defend an estimate of the probability that the individual will engage in criminal behavior. Judges and legislators, however, should decide whether this probability of criminal behavior is sufficient to justify preventive interventions because they are the appropriate persons to weigh competing claims among social values in a democratic society.

Barbara Underwood has asserted that one cannot evaluate the usefulness of prediction from a policy perspective other than in the context of the feasible alternatives to prediction as a basis for making decisions. In the sentencing and parole context, the principal alternative to making decisions on the basis of prediction is making them on retributive grounds. There have been numerous proposals, based in part upon dissatisfaction with the research findings reviewed above, to abandon prediction altogether and limit criminal disposition to consideration of "just deserts" for the crime committed (von Hirsch). The chief difficulty here, however, lies in the assessment of what constitutes "just deserts" for a given criminal behavior. Although the relative ranking of deserved punishments for given crimes is reliable (everyone agrees that murder deserves more punishment than jaywalking), the absolute punishment to be "justly" ascribed is determinable by social consensus only within a broad range. If for no other reason than the lack of any workable alternative, the prediction of criminal behavior is likely to remain an essential aspect of the criminal justice system.

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