Validating the Level of Service Inventory—Revised and the Level of Service Inventory: Screening Version With a Sample of Probationers

Lowenkamp, Christopher T.; Lovins, Brian; Latessa, Edward J.

doi:10.1177/0032885509334755

Cited by 43 publications

(37 citation statements)

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“…Numerous meta‐analytic studies have demonstrated the LSI‐R's efficacy at predicting recidivism in various intuitional and correctional settings (Gendreau, Goggin, and Smith, ; Lowenkamp, Lovins, and Latessa, ; Smith, Cullen, and Latessa, ). Similar to the PCRA, this tool consists of 43 items that reflect the following risk predictive characteristics: criminal history, education and employment, family and marital situation, leisure and recreation, companions, alcohol or drug problems, antisocial patterns, pro‐criminal attitudes, and barriers to release.…”

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confidence: 99%

Does Change in Risk Matter?

Cohen

Lowenkamp

VanBenschoten

2016

Criminology & Public Policy

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Research SummaryThe Post Conviction Risk Assessment (PCRA) is a correctional assessment tool used by federal probation officers that identifies offenders most likely to commit new crimes and the criminogenic characteristics that, if changed, could reduce the likelihood of recidivism. We explored how changes in offender risk influence the likelihood of recidivism by tracking a population of 64,716 offenders placed on federal supervision with multiple PCRA assessments. In general, offenders scoring in the high-, moderate-, and low/moderate-risk categories at their initial assessment and experiencing decreases in their risk classifications were less likely to recidivate compared with their counterparts whose risk levels remained unchanged or increased. Conversely, increases in offender risk were associated with higher rates of reoffending behavior. Notably, we saw no recidivism reduction for offenders in the lowest risk category if they received decreases in their overall PCRA scores. Policy Implications This analysis provides officers with crucial information about how changes in offender risk can influence the likelihood of arrest. Probation officers should consider adjustingThe authors would like to thank Laura Baber and Matthew Rowland at the Administrative Office of the United States Courts for their helpful suggestions and comments. Jeremy Luallen from Abt Associates also provided helpful guidance. Finally, this publication benefited from the careful editing of Ellen Fielding. Direct correspondence to Thomas H. Cohen, Administrative Office of the U.S. Courts, One Columbus Circle NE,

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confidence: 99%

Does Change in Risk Matter?

Cohen

Lowenkamp

VanBenschoten

2016

Criminology & Public Policy

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“…Again, it gains added credibility because it is rooted in the empirical literature on predictors of recidivism and is integrated with a prominent paradigm of correctional treatment-which are the key principles of effective intervention (Cullen, 2002; see also Cullen and Gendreau, 2000;Gendreau et al, 2006;Ogloff and Davis, 2004). Across North America, the LSI-R is in use in more than 900 correctional agencies (Lowenkamp, Lovins, and Latessa, 2009; see also Listwan, Johnson, Cullen, and Latessa, 2008).…”

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Can 14,737 women be wrong? A meta‐analysis of the LSI‐R and recidivism for female offenders*

Smith

Cullen

Latessa

2009

Criminology & Public Policy

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Research Summary Over the past two decades, researchers have been increasingly interested in measuring the risk of offender recidivism as a means of advancing public safety and of directing treatment interventions. In this context, one instrument widely used in assessing offenders is the Level of Service Inventory‐Revised (LSI‐R). Recently, however, the LSI‐R has been criticized for being a male‐specific assessment instrument that is a weak predictor of criminal behavior in females. Through the use of meta‐analytic techniques, we assessed this assertion. A total of 27 effect sizes yielded an average r value of .35 ([confidence interval] CI = .34 to .36) for the relationship of the LSI‐R with recidivism for female offenders (N= 14,737). When available, we also made within‐sample comparisons based on gender. These comparisons produced effect sizes for males and females that were statistically similar. Policy Implications These results are consistent with those generated in previous research on the LSI‐R. They call into question prevailing critiques that the LSI‐R has predictive validity for male but not for female offenders. At this stage, it seems that corrections officials should be advised that the LSI‐R remains an important instrument for assessing all offenders as a prelude to the delivery of treatment services, especially those based on the principles of effective intervention. Critics should be encouraged, however, to construct and validate through research additional gender‐specific instruments that revise, if not rival, the LSI‐R.

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“…Developed in the mid-1980s, the LSI-R is frequently used at both sentencing and probation stages of the criminal justice system to "guide sentencing decisions, placement in correctional programs, institutional assignments, and release from institutional custody." 155 The LSI-R uses fifty-four factors in the "areas of Criminal History, Education and Employment, Financial, Family, Accommodations, Leisure and Recreation, Companions, Alcohol and Drugs, Emotional and Personal Issues, and Attitudes and Orientation." 156 These factors then generate a risk prediction of each offender's likelihood of recidivism.…”

Section: Equal Protection Under Algorithms 327mentioning

confidence: 99%

Equal Protection Under Algorithms: A New Statistical and Legal Framework

Yang¹,

Dobbie²

2020

MLR

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In this Article, we provide a new statistical and legal framework to understand the legality and fairness of predictive algorithms under the Equal Protection Clause. We begin by reviewing the main legal concerns regarding the use of protected characteristics such as race and the correlates of protected characteristics such as criminal history. The use of race and nonrace correlates in predictive algorithms generates direct and proxy effects of race, respectively, that can lead to racial disparities that many view as unwarranted and discriminatory. These effects have led to the mainstream legal consensus that the use of race and nonrace correlates in predictive algorithms is both problematic and potentially unconstitutional under the Equal Protection Clause. This mainstream position is also reflected in practice, with all commonly used predictive algorithms excluding race and many excluding nonrace correlates such as employment and education. Next, we challenge the mainstream legal position that the use of a protected characteristic always violates the Equal Protection Clause. We develop a statistical framework that formalizes exactly how the direct and proxy effects of race can lead to algorithmic predictions that disadvantage minorities relative to nonminorities. While an overly formalistic solution requires exclusion of race and all potential nonrace correlates, we show that this type of algorithm is unlikely to work in practice because nearly all algorithmic inputs are correlated with race. We then show that there are two simple statistical solutions that can eliminate the direct and proxy effects of race, and which are implementable even when all inputs are correlated with race. We argue that our proposed algorithms uphold the principles of the equal protection doctrine because they ensure that individuals are not treated differently on the basis of membership in a protected class, in stark contrast to commonly used algorithms that unfairly disadvantage minorities despite the exclusion of race. We conclude by empirically testing our proposed algorithms in the context of the New York City pretrial system. We show that nearly all commonly used algorithms violate certain principles underlying the Equal Protection Clause by including variables that are correlated with race, generating substantial proxy effects that unfairly disadvantage Black individuals relative to white individuals. Both of our proposed algorithms substantially reduce the number of Black defendants detained compared to commonly used algorithms by eliminating these proxy effects. These findings suggest a fundamental rethinking of the equal protection doctrine as it applies to predictive algorithms and the folly of relying on commonly used algorithms.

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Validating the Level of Service Inventory—Revised and the Level of Service Inventory: Screening Version With a Sample of Probationers

Cited by 43 publications

References 10 publications

Does Change in Risk Matter?

Does Change in Risk Matter?

Can 14,737 women be wrong? A meta‐analysis of the LSI‐R and recidivism for female offenders*

Equal Protection Under Algorithms: A New Statistical and Legal Framework

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