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Using Group Statistics to Sentence Individual Criminals: An Ethical and Statistical Critique of the Virginia Risk Assessment Program

NCJ Number
220937
Journal
Journal of Criminal Law and Criminology Volume: 97 Issue: 3 Dated: Spring 2007 Pages: 699-730
Author(s)
Brian Netter
Date Published
2007
Length
32 pages
Annotation
This paper critiques the Virginia Risk Assessment Program used for sentencing nonviolent criminals.
Abstract
Although not appearing dramatic, the Virginia Risk Assessment Program’s assumptions reflect a monumental change in how criminals are sentenced yet it has been implemented with dangerously little public discussion. A review of the reports and the Virginia data leave one unconvinced that Virginia has adopted an appropriate procedure for sentencing nonviolent criminals. There is strong reason to support vesting sentencing discretion in judges. This method can limit the considerations to factors related to crime deemed deserving of punishment. Virginia’s Risk Assessment Program uses predictions of recidivism to recommend which nonviolent felons should be incarcerated. Virginia’s sentencing scheme depends on a statistical study commissioned by the legislature that purports to match offender characteristics with future behavior. New offenders are given recidivism scores that depend on gender, employment status, marital status, and age, all factors seemingly unrelated to the criminal conduct itself. It is suggested that the program presents serious issues regarding the ethical propriety of predictive techniques in criminal sentencing. This paper evaluates Virginia’s Risk Assessment Program as a means to probe a sense of discomfort with the use of predictive group statistics on individual criminals and to determine whether the statistical techniques adopted in Virginia are suitable for this important task. The paper also provides a history of risk assessment in Virginia, discusses the ethics of predictive sentencing of this type and critiques the methodology of Virginia’s approach and the errors that come about.

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