Actuarial Sentencing: An “Unsettled” Proposition

Abstract

This paper discusses the concerns associated with the introduction of, and increased reliance on, actuarial risk tools in sentencing in order to: (1) stimulate cross-disciplinary dialog and research about the impact of incorporating actuarial risk logic into sentencing processes and (2) identify questions requiring further empirical examination. In this article, I recognize that actuarial risk logic offers managerial and organizational benefits, but I also demonstrate that the application of actuarial risk when sentencing offenders is not without important consequences. First, I provide a brief outline of the emergence, logic, and entrenchment of probabilistic reasoning within criminal justice decision-making, and the more recent extension and application of actuarial risk logic to sentencing. Then, I use the following themes to define the limits of using risk sciences in sentencing: (1) the logical structure of risk; (2) the slippage between risk prediction and individual causation; (3) current methodological limits of risk science; (4) the potential for gender and race discrimination; (5) the legal relevance and transparency of risk-based sentencing; and (6) the jurisprudential and organizational impact of various risk technologies. Importantly, the nature and severity of these complications will vary by, and within, the jurisdiction (or sentencing regime) because current sentencing practices are influenced by local jurisdictional needs and sentencing laws.

Keywords:

• risk
• sentencing
• punishment
• disparity
• actuarial justice
• theory
• gender
• race

Notes

1. Hong Kong is in the process of considering the adoption of risk instruments in sentencing (correspondence with the author September 2011).

2. For a British example see http://www.glasgow.gov.uk/en/Residents/Care_Support/CriminalJustice/Assessmentandreports/ (accessed 30 December 2011)

3. The Senate Bill 1161 of 2010 directed the Sentencing Commission to create a method for assessing offender risk at sentencing. To address this mandate, the Pennsylvania Sentencing Commission has undertaken the Risk Assessment Project (see: http://pcs.la.psu.edu/publications/research-and-evaluation-reports/risk-assessment—accessed 30 December 2011).

4. See also Ostrom et al., 2002.

5. Some American jurisdictions using risk assessment for presentence reports or sentencing include: California, Indiana, Missouri, New York, Pennsylvania, Virginia, and Washington.

6. For example, in Canada see: R. v. Elliot; R. v. B.H.D.; in the USA see: Malenchik v. Indiana, No. 79S02-0908-CR-365 (Ind. 9 June 2010).

7. Fitzgibbon (2007) argued that the introduction of risk assessment has led to a deskilling of probation officers and other professionals.

8. Although I refer here to the LSI, readers need to be aware that several versions of this tool exist. As noted in Maurutto and Hannah-Moffat (2006, p. 439) the LSI, originally developed in Ontario, Canada in the late 1970s, “quickly developed international notoriety. Currently, the tool is used in jurisdictions throughout Canada, the United States, the United Kingdom and Australia, among others. The tool, originally written in English, is available in Spanish, Croatian and French (French European and French Canadian), and it is in the process of being translated into Dutch and Icelandic.” This information was obtained from correspondence with Multi-Health Systems Inc., 22 September 2003.

9. Data sources that inform my analysis include: international research on risk; criminal justice policy; a selection of Canadian legal cases; a content analysis of common risk tools, their user manuals, and interview guides; and interviews with 127 practitioners (crown attorneys; defense lawyers; policy-makers; probation officers; prison staff; and risk instrument developers, trainers, and researchers).

10. Andrews and Bonta (2006) provide a detailed description of these tools and this development.

11. Some tools, such as the Youth Level of Service Inventory, include “protective factors,” which are positive influences that can improve the lives of individuals or enhance public safety. These factors (e.g. stable employment or housing, access to social services, positive self-esteem, and positive attitudes, values, or beliefs) can decrease the likelihood of recidivism and counterbalance risk factors. Additional factors for youth are parental supervision and strong parenting skills, social support, and positive role models and peer groups.

12. See Ward and Maruna (2007) for a fuller critique of RNR. Critical literature about punishment includes a lively debate about the “what works” analyzes of the logic of RNR and cognitive behavioralism (Ward & Stewart, 2003).

13. The targeting of interventions is linked to the responsivity principle, which refers to the matching of styles and modes of intervention to the learning styles and abilities of offenders (Andrews et al., 1990, p. 20). It requires attention to: (a) how diverse populations respond to various treatment options and (b) “specific” responsivity factors (e.g. self-esteem, motivation, personality traits, life circumstances, and therapeutic relationships) that may facilitate or impede an individual’s response to intervention (Ogloff & Davis, 2004, p. 233).

14. A long tradition of sociological research demonstrates that social stratification and poverty are related to both criminality and criminalization.

15. See Hannah-Moffat (2004) for a more comprehensive analysis of the concept of criminogenic need.

16. The phrase “trust in numbers” is borrowed from Porter (1996).

17. For a more comprehensive discussion of this debate and present developments, see Van Voorhis et al. (2010).

18. For an elaboration of this argument, see Hannah-Moffat (2009). Such analyses highlight the fact that men and women have different needs and represent different kinds of risk, and note the importance of considering “gender-specific needs” and the specific needs of minority women.

19. That is: education/employment; family/marital relations; leisure/recreational involvement; criminal acquaintances; attitudes toward crime; and substance abuse—which are six of the eight risk/need factors in the LSI-OR.

20. As Van Voorhis et al. (2010, p. 262) documented, a number of studies have reported that dynamic risk assessments such as the LSI-R are valid for women.

21. For a more comprehensive human rights analysis: see CHRC (2003); this can be accessed at http://www.chrc-ccdp.ca/legislation_policies/consultation_report-en.asp.

22. Comparable restrictions exist internationally; the 1991 Criminal Justice Act in England and Wales also explicitly prohibits “discrimination in sentencing on the basis of a number of offender characteristics, including race” (Raynor & Lewis, 2011, p. 1358). In Canada, the Canadian Charter of Rights and Freedoms (sec. 15) would apply.

23. See, e.g. Petersilia and Turner (1987); the authors found that omitting factors that are correlated with race from a model to predict recidivism reduced the accuracy of the model by 5-12 percentage points (cited in Oleson, 2011).

24. Risk manuals acknowledge that adjustments (overrides) may be required, but state that these adjustments should not be required in more than 10% of cases.

25. Also see the reasoning of the court in US District Court Eastern District of New York against C.R. 09-CR-155; http://sentencing.typepad.com/files/us-v-cr-eeinstein-sentencing_memorandum-final.pdf (accessed 30 December 2011).

26. Although evidence on the concordance between pre-sentence report (PSR) and sentencing is contested, highly nuanced, and difficult to unravel empirically (Tata, Burns, Halliday, Hutton, & McNeill, 2008), previous research has demonstrated that the PSR plays a central interpretive role in sentencing and among criminal justice professionals.

27. Cases in which individuals are legally designated as “dangerous offenders” have generated considerably more debate among experts about how opinions about “danger and risk” are formulated.

28. Malenchik v. Indiana, No. 79S02-0908-CR-365 (Ind. 9 June 2010).

29. This is particularly the case when a jurisdiction explicitly prioritizes proportionality in sentencing, i.e. the Canadian Youth Criminal Justice Act; see Maurutto and Hannah-Moffat (2007). Some authors have argued that this conflict is not as problematic as it seems, see, e.g. Marcus (2009a, 2009b).

30. Researchers have yet to investigate how judges and practitioners use risk information in decision-making. Interestingly, studies have revealed that judges and legal practitioners are generally supportive of risk instruments, but that they know little about risk technologies (Bonta, Bourgon, Jesseman, & Yessine, 2005). Additionally, judges reportedly often use information that fit their perceptions of risk (Scott, 2008), but little is known about what factors judges actually consider in their own professional assessments of risk (Vigorita, 2003).