Alexander M. Holsinger

Bio: Alexander M. Holsinger is an academic researcher from University of Missouri–Kansas City. The author has contributed to research in topics: Predictive validity & Risk assessment. The author has an hindex of 18, co-authored 33 publications receiving 1665 citations. Previous affiliations of Alexander M. Holsinger include University of Missouri.

Analyzing Bond Supervision Survey Data: The Effects of Pretrial Detention on Self-Reported Outcomes

Abstract: INCREASING ATTENTION HAS been paid to the functioning and effects of pretrial practices, particularly the use of pretrial detention. Building on a growing body of research, scholars and policy makers have engaged in a number of endeavors designed to maximize the effectiveness of pretrial case processing and decision making. Some of the first large-scale quantitative examinations of pretrial decision making involved what effect pretrial detention might have on relevant justice outcomes, such as conviction and sentencing (e.g., guilt vs. innocence; sentences to incarceration vs. community; length of sentence) (Goldkamp, 1979; Goldkamp & Gottfredson, 1979; Leipold, 2005; Oleson, Lowenkamp, Wooldredge, VanNostrand, & Cadigan, 2017; Rankin, 1964; Sacks & Ackerman, 2014; Williams, 2003). The current study examines the length of pretrial detention and its potential impact on outcomes that are directly related to functionality and may be indirectly related to other justicespecific outcomes. In recent years attention has focused on the development and implementation of actuarial risk assessment procedures. The advent of risk assessments in theory reduces subjectivity and allows for a more scientific, informed decision process that incorporates the measurement and management of risk (Lowenkamp, Lemke, & Latessa, 2008; Lowenkamp & VanNostrand, 2013). This in turn (again in theory) allows for the best, most efficient use of limited and expensive jail space. It makes sense to ensure that limited jail space is reserved for those who pose the highest risk of either failure to appear (FTA) or new criminal activity (NCA). Actuarial risk assessment has the potential for ensuring that the highest risk individuals are most likely to be detained in jail, while lower risk defendants remain in the community (Austin, Ocker, & Bhati, 2010; Bechtel, Lowenkamp, & Holsinger, 2011; VanNostrand, 2003). Of most recent import, the effect of relatively short pretrial detention, regardless of risk level, is being considered in terms of its potential negative effect on other outcomes besides conviction and sentencing. While the effects of long-term incarceration have been well documented (see for example Liem, 2016; Western, 2002; and Western & Pettit, 2000), less is known regarding the specific effects of pretrial detention on what may be considered less obvious outcomes. Even a short stay in jail may have a disrupting effect on the lives of individuals regarding their employment, housing, custody of minor children, and a host of other factors. Complicating matters is a bail system that likely causes those who have been arrested for low-level crimes to be held when they are not able to post even a meager amount of bail. Because of this, jails in general and the monetary bail system in particular may represent a point at which the criminal justice system becomes “stickier” for lower socio-economic income groups. Gaining an actuarial riskbased profile of those who remain in jail can be revelatory; individuals who do not pose much if any risk, yet are unable to post bail for any number of reasons, may end up detained in jail pretrial.

Felons in Possession: Examining the Impact of a Public Service Advertising Campaign and Sentence Enhancements

Abstract: This paper examines the penetration impact of a multifaceted, deterrence-oriented public service advertisement campaign conducted in Kansas City. The campaign was designed to inform people with felonies about mandatory minimum sentencing related to being caught in possession of firearms. People under correctional supervision in the community completed self-report surveys regarding their awareness of federal sentence enhancements, their beliefs about future behaviors, and the impact such enhancements may have on future offending. Results from the survey of felons in the community indicate a high level of awareness of this campaign, and that the sentence enhancements significantly impacted their perceptions of criminal sanctioning.

Implementation of Actuarial Risk/Need Assessment and its Effect on Community Supervision Revocations

Abstract: While studies examining the predictive validity of the Level of Service Inventory — Revised (LSI-R) are useful and should continue, more research needs to be done in the area of systemic change: Once the LSI-R has been adopted and fully integrated into an agency's work, what systemic changes may occur? Does implementing a third-generation risk/need assessment tool, for example, truly facilitate an agency's ability to abide by the “risk principle” of correctional intervention? This article has two central objectives. First, the LSI-R composite score is used to predict case outcome using several years' worth of data from a large Midwestern city. The data were gathered from the agencies that conduct supervision in the community. The statistical relationship between the LSI-R score and case outcome (success vs. failure) is investigated and assessed. Second, the article provides a cursory look at rates of revocation plus transfer to prison over time (post LSI-R implementation).

A Rejoinder to Dressel and Farid: New Study Finds Computer Algorithm is More Accurate than Humans at Predicting Arrest and as Good as a Group of 20 Lay Experts

Abstract: IN A RECENT article1 published in Science Advances, Dressel & Farid (2018) presented results from their recent study that they believe call into question the accuracy and fairness of the COMPAS risk assessment tool specifically and all statistically-based prediction tools more generally. In reaching these two conclusions, Dressel and Farid made the arguments that laypeople are as accurate (or better) and as fair in their prediction of reoffending as statistically based risk assessment instruments empirically designed to predict reoffending. In the following pages, we closely examine the authors’ premise, methodology, and conclusions, focusing on some omissions and incorrect assumptions. In addition, while Dressel and Farid focus on the binary decision of “future crime” (yes vs. no), we also argue that risk assessment has important justice related objectives beyond merely predicting new criminal conduct. We also think it is worth noting that none of us has any ties to COMPAS or its parent company Northpointe. This rebuttal is not meant as an endorsement of COMPAS.

Correctional Services Corporation v. Malesko: Boss, “They Can’t Hurt You Now”:

Abstract: Over the past 20 years, issues surrounding the privatization of various services within correctional settings have been revisited in the United States and internationally. Almost without question, the motivation for implementing privatization of various services within a correctional setting (if not the entirety of an institution, for example) is by nature fiscal. The area of corrections has been the subject of much debate and growth regarding privatization. Yet the newest dawn of the private prison has left many questions unanswered. The answers will likelybe found through litigation on the federal level, and most of the questions will likely deal with issues of liability and culpability. This article focuses on the recent Supreme Court case ofCorrectional Services Corporation v. Malesko (2001). This case addressed the use of afederal civil rights remedy, a Bivens action, against a private correctional facility. As such,the case opinion helps to settle some of the issues surrounding the liability of priv...

The Recent Past and Near Future of Risk and/or Need Assessment

Abstract: The history of risk assessment in criminal justice has been written on several occasions (Andrews & Bonta, 2003; Clements, 1996; Hollin, 2002). Here we assess progress since Andrews, Bonta, and Hoge’s (1990; Andrews, Zinger, et al., 1990) statement of the human service principles of risk-needresponsivity (RNR) and professional discretion. In those articles, the corrections-based terms of risk and need were transformed into principles addressing the major clinical issues of who receives treatment (higher risk cases), what intermediate targets are set (reduce criminogenic needs), and what treatment strategies are employed (match strategies to the learning styles and motivation of cases: the principles of general and specific responsivity). General responsivity asserts the general power of behavioral, social learning, and cognitive-behavioral strategies. Specific responsivity suggests matching of service with personality, motivation, and ability and with demographics such as age, gender, and ethnicity. Nonadherence is possible for stated reasons under the principle of professional discretion. Expanded sets of principles now include consideration of case strengths, setting of multiple criminogenic needs as targets, community-based, staff relationship and structuring skills, and a management focus on integrity through the selection, training, and clinical supervision of staff and organizational supports (Andrews, 2001). The review is conducted in the context of the advent of the fourth generation of offender assessment. Bonta (1996) earlier described three generations of risk assessment. The first generation (1G) consisted mainly of unstructured professional judgments of the probability of offending behavior. A

Associations of parenting dimensions and styles with externalizing problems of children and adolescents: An updated meta-analysis.

Abstract: The present meta-analysis integrates research from 1,435 studies on associations of parenting dimensions and styles with externalizing symptoms in children and adolescents. Parental warmth, behavioral control, autonomy granting, and an authoritative parenting style showed very small to small negative concurrent and longitudinal associations with externalizing problems. In contrast, harsh control, psychological control, authoritarian, permissive, and neglectful parenting were associated with higher levels of externalizing problems. The strongest associations were observed for harsh control and psychological control. Parental warmth, behavioral control, harsh control, psychological control, autonomy granting, authoritative, and permissive parenting predicted change in externalizing problems over time, with associations of externalizing problems with warmth, behavioral control, harsh control, psychological control, and authoritative parenting being bidirectional. Moderating effects of sampling, child's age, form of externalizing problems, rater of parenting and externalizing problems, quality of measures, and publication status were identified. Implications for future research and practice are discussed. (PsycINFO Database Record

The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning.

Abstract: The nascent field of fair machine learning aims to ensure that decisions guided by algorithms are equitable. Over the last several years, three formal definitions of fairness have gained prominence: (1) anti-classification, meaning that protected attributes---like race, gender, and their proxies---are not explicitly used to make decisions; (2) classification parity, meaning that common measures of predictive performance (e.g., false positive and false negative rates) are equal across groups defined by the protected attributes; and (3) calibration, meaning that conditional on risk estimates, outcomes are independent of protected attributes. Here we show that all three of these fairness definitions suffer from significant statistical limitations. Requiring anti-classification or classification parity can, perversely, harm the very groups they were designed to protect; and calibration, though generally desirable, provides little guarantee that decisions are equitable. In contrast to these formal fairness criteria, we argue that it is often preferable to treat similarly risky people similarly, based on the most statistically accurate estimates of risk that one can produce. Such a strategy, while not universally applicable, often aligns well with policy objectives; notably, this strategy will typically violate both anti-classification and classification parity. In practice, it requires significant effort to construct suitable risk estimates. One must carefully define and measure the targets of prediction to avoid retrenching biases in the data. But, importantly, one cannot generally address these difficulties by requiring that algorithms satisfy popular mathematical formalizations of fairness. By highlighting these challenges in the foundation of fair machine learning, we hope to help researchers and practitioners productively advance the area.

The Effectiveness of Correctional Rehabilitation: A Review of Systematic Reviews

Abstract: The effects of correctional interventions on recidivism have important public safety implications when offenders are released from probation or prison. Hundreds of studies have been conducted on those effects, some investigating punitive approaches and some investigating rehabilitation treatments. Systematic reviews (meta-analyses) of those studies, while varying greatly in coverage and technique, display remarkable consistency in their overall findings. Supervision and sanctions, at best, show modest mean reductions in recidivism and, in some instances, have the opposite effect and increase reoffense rates. The mean recidivism effects found in studies of rehabilitation treatment, by comparison, are consistently positive and relatively large. There is, however, considerable variability in those effects associated with the type of treatment, how well it is implemented, and the nature of the offenders to whom it is applied. The specific sources of that variability have not been well explored, but some princ...