The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

weight information in an appropriate manner. Furthermore, clinical prediction is not
standardized. Bonta (1996) notes that clinical decision rules are not easily observable or
replicable.
This is not to say that clinical predictions are not useful, nor that statistical
prediction methods are without flaws.
Statistical predictions by definition provide
average results, and the experience <strong>of</strong> criminal justice pr<strong>of</strong>essionals plays an important
role in highlighting deviations from the mean. In fact, pr<strong>of</strong>essional override is the littlediscussed
fourth ‘principle <strong>of</strong> effective intervention’ (Andrews, Bonta, & Hoge, 1990).
Some critics <strong>of</strong> statistical models have also pointed out the ethical concerns about
imposing such impersonal judgments on <strong>of</strong>fenders with individualized needs (see
Gottfredson & Jarjoura, 1996, for a review <strong>of</strong> the criticisms <strong>of</strong> statistical models). One
important and much-discussed ethical concern about risk prediction highlighted by
Gottfredson and Jarjoura is that many risk factors <strong>of</strong> crime are highly correlated with
race. This makes agency staff fearful <strong>of</strong> taking them into account in decision making.
However, Gottfredson and Moriarty (2006) defend statistical risk assessment, noting its
importance at the “nexus <strong>of</strong> research and practice,” and pointing out that: “Properly
developed and implemented, risk assessment devices can impose criminal justice
decision making, properly target and potentially save resources, and potentially increase
the public safety” (p. 195). Gottfredson and Jarjoura (ibid.) also set out a solution for
reducing the bias in risk assessment. Although the role <strong>of</strong> pr<strong>of</strong>essional override cannot
be discounted, both papers argue that ignoring important predictive variables because <strong>of</strong>
ethical concerns, rather than investigating how prediction instruments can empirically
deal with these difficult issues, severely limits the utility <strong>of</strong> predictive devices and thus
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