By Beth Karp

Several widely used criminal risk assessment instruments factor a defendant’s abstract beliefs, peer associations, and family relationships into their risk scores. The inclusion of those factors is empirically unsound and raises profound ethical and constitutional questions. This Article is the first instance of legal scholarship on criminal risk assessment to (a) conduct an in-depth review of risk assessment questionnaires, scoresheets, and reports, and (b) analyze the First and Fourteenth Amendment implications of attitude and associational factors. Additionally, this Article challenges existing scholarship by critiquing widely accepted but dubious empirical justifications for the inclusion of attitude and associational items. The items are only weakly correlated with recidivism, have not been shown to be causal, and have in fact been shown to decrease the predictive validity of risk assessment instruments. Quantification of attitudes and associations should cease unless and until it is done in a way that is empirically sound, more useful than narrative reports, and consistent with the First and Fourteenth Amendments.

Stanford Law Review, Vol. 75, 2023, 99p.

By Roland Neil, and Michael Zanger-Tishler

There is great concern about algorithmic racial bias in the risk assessment instruments (RAIs) used in the criminal legal system. When testing for algorithmic bias, most research effectively uses arrest data as an unbiased measure of criminal offending, which collides with longstanding concerns that arrest is a biased proxy of offending. Given the centrality of arrest data in RAIs, racial differences in how arrest proxies offending may be a key pathway through which RAIs become biased. In this review, we evaluate the extensive body of research on racial differences in arrest as a measure of crime. Furthermore, we detail several ways that racial bias in arrest records could create algorithmic bias, although little research has attempted to measure the degree of algorithmic bias generated by using racially biased arrest records. We provide a roadmap to assist future research in understanding the impact of biased arrest records on RAIs.

Annual Review of Criminology, Vol. 8:97-119 January 2025)