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Evidence-based Practices (EBP) - Principle 1. Assess Risk and Needs

Police, prosecutors, judges, and other criminal justice actors increasingly use algorithmic risk assessment to estimate the likelihood that a person will commit future crime. As many scholars have noted, these algorithms tend to have disparate racial impacts. In response, critics advocate three strategies of resistance: (1) the exclusion of input factors that correlate closely with race; (2) adjustments to algorithmic design to equalize predictions across racial lines; and (3) rejection of algorithmic methods altogether.

This Article’s central claim is that these strategies are at best superficial and at worst counterproductive because the source of racial inequality in risk assessment lies neither in the input data, nor in a particular algorithm, nor in algorithmic methodology per se. The deep problem is the nature of prediction itself. All prediction looks to the past to make guesses about future events. In a racially stratified world, any method of prediction will project the inequalities of the past into the future. This is as true of the subjective prediction that has long pervaded criminal justice as it is of the algorithmic tools now replacing it. Algorithmic risk assessment has revealed the inequality inherent in all prediction, forcing us to confront a problem much larger than the challenges of a new technology. Algorithms, in short, shed new light on an old problem.

Ultimately, the Article contends, redressing racial disparity in prediction will require more fundamental changes in the way the criminal justice system conceives of and responds to risk. The Article argues that criminal law and policy should, first, more clearly delineate the risks that matter and, second, acknowledge that some kinds of risk may be beyond our ability to measure without racial distortion—in which case they cannot justify state coercion. Further, to the extent that we can reliably assess risk, criminal system actors should strive whenever possible to respond to risk with support rather than restraint. Counterintuitively, algorithmic risk assessment could be a valuable tool in a system that supports the risky.

This paper explains the science underlying risk-based decision-making and explores both the promise and controversies associated with the increasing application of “big data” to the field of criminal justice. 

These presentation slides should be read before anyone begins to investigate which risk/needs instrument to use in their agency or organization. Topics covered include: prevalence of structured risk/need instruments; evaluating risk/need instruments; issues in predictive validity meta-analyses; apples and oranges—fundamentally dissimilar instruments; how instrument characteristics impact predictive validity; 12 other critical distinctions among risk/need instruments; black-and-white versus shades of gray—overreliance on binary decision making; irrelevance of binary models in criminal justice settings; burden of proof—statistical support for differences among instruments; Singh et al. (2011) comparison of nine risk/need instruments--an example of margins of error, graphical representations of predictive validity, and re-analysis; and recommendations—how to compare, select, and evaluate risk/need instruments.

Evaluating the Predictive Validity of Risk/Need Assessments: Recommendations for Correctional Agencies and Criminal Justice Researchers Cover

If you are contemplating the use of another agency’s pretrial risk assessment tool without modification to your own organization’s needs you may want to read this report. The use of pretrial risk assessment and pretrial supervision are examined in this report. Since there has been little to no compatibility found between studies of risk assessment tool utilization, it is suggested that the application of an instrument from one jurisdiction to another probably will not work. The same applies to the use of pretrial supervision programs.

Overview of Research Findings on Pretrial Risk Assessment and Pretrial Supervision Cover

A program assessment, the first component of the larger evidence-based policy making framework developed by the Pew-MacArthur Results First Initiative, is a three-step process to help policymakers address key questions about their programs.

The past several years have seen a surge of interest in using risk assessment in criminal sentencing, both to reduce recidivism by incapacitating or treating high-risk offenders and to reduce prison populations by diverting low-risk offenders from prison. We begin by sketching jurisprudential theories of sentencing, distinguishing those that rely on risk assessment from those that preclude it. We then characterize and illustrate the varying roles that risk assessment may play in the sentencing process. We clarify questions regarding the various meanings of “risk” in sentencing and the appropriate time to assess the risk of convicted offenders. We conclude by addressing four principal problems confronting risk assessment in sentencing: conflating risk and blame, barring individual inferences based on group data, failing adequately to distinguish risk assessment from risk reduction, and ignoring whether, and if so, how, the use of risk assessment in sentencing affects racial and economic disparities in imprisonment.

“The Sex Offender Treatment Intervention and Progress Scale (SOTIPS) is a statistically-derived dynamic measure designed to aid clinicians, correctional caseworkers, and probation and parole officers in assessing risk, treatment and supervision needs, and progress among adult males who have been convicted of one or more qualifying sexual offenses and committed at least one of these sexual offenses after their 18th birthday … SOTIPS item scores are intended to reflect an individual's relative treatment and supervision needs on each risk factor. The SOTIPS total score is intended to provide an estimation of an individual's overall level of dynamic risk and need for supervision and treatment” (p. 1). Sections of this manual include: overview and administration; item descriptions and scoring criteria; and the SOTIPS scoring sheet.

SOTIPS: Sex Offender Treatment Intervention and Progress Scale Cover

No program or intervention can be expected to work for everyone. Providing too much or the wrong kind of services not only fails to improve outcomes, but it can make outcomes worse by placing excessive burdens on some participants and interfering with their engagement in productive activities, like work or school. This is the foundation for a body of evidence-based principles referred to as risk, need, responsivity, or RNR (Andrews & Bonta, 2010). RNR is derived from decades of research demonstrating that the best outcomes are achieved in the criminal justice system when (1) the intensity of criminal justice supervision is matched to participants’ risk for criminal recidivism or likelihood of failure in rehabilitation (criminogenic risk) and (2) interventions focus on the specific disorders or conditions that are responsible for participants’ crimes (criminogenic needs) (Andrews et al., 1990, 2006; Gendreau et al., 2006; Lipsey & Cullen, 2007; Lowenkamp et al., 2006a, 2006b; Smith et al., 2009; Taxman & Marlowe, 2006). Moreover, mixing participants with different levels of risk or need in the same treatment groups or residential programs has been found to increase crime, substance use, and other undesirable outcomes, because it exposes low-risk participants to antisocial peers and values (e.g., Lloyd et al., 2014; Lowenkamp & Latessa, 2004; Lowenkamp et al., 2005; Welsh & Rocque, 2014; Wexler et al., 2004).

Despite compelling evidence validating these RNR principles, many behavioral health and criminal justice professionals misconstrue the concepts of risk, need, and responsivity, leading them to deliver the wrong services to the wrong persons and in the wrong order. Even with the best of intentions to follow evidence-based practices, many programs inadvertently waste precious resources, frustrate consumers, and deliver lackluster results. To enhance program effectiveness and efficiency, it is necessary to translate these research-based principles into terms that are familiar to many practitioners, to help them select the most appropriate interventions under the right circumstances. 

Taxman, Faye S. Federal Probation v. 68 no. 2, p. 31-35

This article explains “the active participant model wherein the offender is part of the decisionmaking process for examining the risk, needs and community factors that affect his or her involvement in criminal behavior. Information on the punishment associated with imprisonment; Explanation on the reentry process; Major themes which are critical to the offender assuming responsibility for his/her actions.”

The Pew-MacArthur Results First Initiative, a project of The Pew Charitable Trusts and the John D. and Catherine T. MacArthur Foundation, works with states and localities to develop the tools policymakers need to identify and fund effective programs that yield high returns on investment.

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