Christopher T. Lowenkamp

Bio: Christopher T. Lowenkamp is an academic researcher from University of Missouri–Kansas City. The author has contributed to research in topics: Recidivism & Risk assessment. The author has an hindex of 33, co-authored 83 publications receiving 4108 citations. Previous affiliations of Christopher T. Lowenkamp include University of Cincinnati & Government of the United States of America.

Gender and the predictive validity of the LSI-R: A study of parolees and probationers.

Abstract: The Level of Service Inventory—Revised (LSI-R) is a classification instrument used to identify the risks and needs of offenders. Originally validated for use with male offenders, some scholars have questioned the instrument’s suitability for use with female offenders. The current study attempts to contribute to the discussion on gender and the predictive validity of the LSI-R. A sample of 2,849 probationers and parolees were administered the LSI-R at two points in time. This design allows for the analysis of the instrument’s predictive validity at Time 1 and Time 2, and of the impact that changes in LSI-R scores may have on rates of recidivism. The results suggest that the LSI-R is a valid instrument for predicting recidivism with male and female offenders and thus should be viewed as a useful resource for practitioners engaged in correctional treatment.

Application of the Risk Principle to Female Offenders

Abstract: Previous research has supported the significance of the principles of effective intervention in correctional treatment. The risk principle suggests that intensive correctional interventions be reserved for higher risk offenders. Increasingly, there is discussion about the application of the risk principle to specialized populations, such as female offenders. The purpose of this article is to test the risk principle on a sizeable sample of female offenders involved in community corrections in a midwestern state. Findings suggest that the risk principle is applicable to women as higher risk female offenders who participated in residential treatment showed lower probability of recidivism than a risk-controlled comparison group, while lower-risk women increased in likelihood of re-arrest after exposure to the same treatment. Results contribute to the growing literature on effective treatment interventions for female offenders.

Risk, Race, & Recidivism: Predictive Bias and Disparate Impact

Abstract: One way to unwind mass incarceration without compromising public safety is to use risk assessment instruments in sentencing and corrections. Although these instruments figure prominently in current reforms, critics argue that benefits in crime control will be offset by an adverse effect on racial minorities. Based on a sample of 34,794 federal offenders, we examine the relationships among race, risk assessment (the Post Conviction Risk Assessment [PCRA]), and future arrest. First, application of well-established principles of psychological science revealed little evidence of test bias for the PCRA — the instrument strongly predicts arrest for both Black and White offenders and a given score has essentially the same meaning — i.e., same probability of recidivism — across groups. Second, Black offenders obtain higher average PCRA scores than White offenders (d= 0.34; 13.5% non-overlap in groups’ scores), so some applications could create disparate impact. Third, most (66%) of the racial difference in PCRA scores is attributable to criminal history — which is already embedded in sentencing guidelines. Finally, criminal history is not a proxy for race, but instead mediates the relationship between race and future arrest . Data are more helpful than rhetoric, if the goal is to improve practice at this opportune moment in history.

Adhering to the Risk and Need Principles: Does It Matter for Supervision-Based Programs?

Abstract: IN THE PAST 20 YEARS, there has been a re-emergence of interest in the effectiveness of correctional treatment programs for offenders. This interest has led to the development of the principles of effective interventions (Gendreau, 1996; Gendreau, French, & Taylor, 2002). Research has now shown a link between these program characteristics and effectiveness (Andrews & Dowden, 1999; Lipsey & Wilson, 1995; Gendreau, 1996; Lowenkamp, 2004: Lowenkamp, Latessa, and Smith, 2006). However, most of these studies have examined traditional residential treatment programs. Therefore, the question remains: Do these principles apply to community non-residential programs such as intensive supervision probation? The current study examines the effects of program characteristics on recidivism using a sample drawn from community non-residential programs to determine if the risk and need principles apply to traditional supervision-oriented programs such intensive supervision probation, electronic monitoring, day reporting, and work release.

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Abstract: Black box machine learning models are currently being used for high-stakes decision making throughout society, causing problems in healthcare, criminal justice and other domains. Some people hope that creating methods for explaining these black box models will alleviate some of the problems, but trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practice and can potentially cause great harm to society. The way forward is to design models that are inherently interpretable. This Perspective clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications where interpretable models could potentially replace black box models in criminal justice, healthcare and computer vision. There has been a recent rise of interest in developing methods for ‘explainable AI’, where models are created to explain how a first ‘black box’ machine learning model arrives at a specific decision. It can be argued that instead efforts should be directed at building inherently interpretable models in the first place, in particular where they are applied in applications that directly affect human lives, such as in healthcare and criminal justice.

The culture of control: crime and social order in contemporary, society

Abstract: Nos últimos trinta trinta anos, houve profundas mudanças na forma como compreendemos o crime e a justiça criminal. O crime tornou-se um evento simbólico, um verdadeiro teste para a ordem social e para as políticas governamentais, um desafio para a sociedade civil, para a democracia e para os direitos humanos. Segundo David Garland, professor da Faculdade de Direito da New York University, um dos principais autores no campo da Sociologia da Punição e com artigo publicado na Revista de Sociologia e Política , número 13, na modernidade tardia houve uma verdadeira obsessão securitária, direcionando as políticas criminais para um maior rigor em relação às penas e maior intolerância com o criminoso. Há trinta anos, nos EUA e na Inglaterra essa tendência era insuspeita. O livro mostra que os dois países compartilham intrigantes similaridades em suas práticas criminais, a despeito da divisão racial, das desigualdades econômicas e da letalidade violenta que marcam fortemente o cenário americano. Segundo David Garland, encontram-se nos dois países os “mesmos tipos de riscos e inseguranças, a mesma percepção a respeito dos problemas de um controle social não-efetivo, as mesmas críticas da justiça criminal tradicional, e as mesmas ansiedades recorrentes sobre mudança e ordem sociais”1 (GARLAND, 2001, p. 2). O argumento principal da obra é o seguinte: a modernidade tardia, esse distintivo padrão de relações sociais, econômicas e culturais, trouxe consigo um conjunto de riscos, inseguranças e problemas de controle social que deram uma configuração específica às nossas respostas ao crime, ao garantir os altos custos das políticas criminais, o grau máximo de duração das penas e a excessivas taxas de encarceramento.

Semantics derived automatically from language corpora contain human-like biases

Abstract: Machine learning is a means to derive artificial intelligence by discovering patterns in existing data. Here, we show that applying machine learning to ordinary human language results in human-like semantic biases. We replicated a spectrum of known biases, as measured by the Implicit Association Test, using a widely used, purely statistical machine-learning model trained on a standard corpus of text from the World Wide Web. Our results indicate that text corpora contain recoverable and accurate imprints of our historic biases, whether morally neutral as toward insects or flowers, problematic as toward race or gender, or even simply veridical, reflecting the status quo distribution of gender with respect to careers or first names. Our methods hold promise for identifying and addressing sources of bias in culture, including technology.

A Survey on Bias and Fairness in Machine Learning

Abstract: With the widespread use of AI systems and applications in our everyday lives, it is important to take fairness issues into consideration while designing and engineering these types of systems. Such systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that the decisions do not reflect discriminatory behavior toward certain groups or populations. We have recently seen work in machine learning, natural language processing, and deep learning that addresses such challenges in different subdomains. With the commercialization of these systems, researchers are becoming aware of the biases that these applications can contain and have attempted to address them. In this survey we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined in order to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and how they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments

Abstract: Recidivism prediction instruments (RPIs) provide decision-makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time. Although such instruments are gaining increasing popularity across the country, their use is attracting tremendous controversy. Much of the controversy concerns potential discriminatory bias in the risk assessments that are produced. This article discusses several fairness criteria that have recently been applied to assess the fairness of RPIs. We demonstrate that the criteria cannot all be simultaneously satisfied when recidivism prevalence differs across groups. We then show how disparate impact can arise when an RPI fails to satisfy the criterion of error rate balance.