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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.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the Level of Service Inventory-Revised (LSI-R) risk/need assessment for Native American and non-native American offenders as well as male and female offenders were investigated and compared.

86 citations

01 Jan 2002
TL;DR: In this article, the authors present a large-scale project that could not have been completed without the efforts and cooperation of many programs, agencies, and individuals, and they wish to thank the directors and staff at the programs included in this project.
Abstract: ii ACKNOWLEDGMENTS This was a large-scale project that could not have been completed without the efforts and cooperation of many programs, agencies, and individuals. The authors wish to thank the directors and staff at the programs included in this project. They were willing to answer questionnaires, surveys, and sit through lengthy interviews to assist in the collection of information and data for this project. iii We also wish to thank a number of people at the University of Cincinnati who devoted countless hours to the completion of this project:

85 citations

Journal ArticleDOI
TL;DR: This paper used the Youth Level of Service Case Management Inventory (YLS/CMI) to assess, classify, and assist agencies with developing treatment and service plans according to the offender's criminogenic risk factors.
Abstract: The purpose of the Youth Level of Service Case Management Inventory (YLS/CMI) is to assess, classify, and assist agencies with developing treatment and service plans according to the offender's criminogenic risk factors. Given the limited research in the predictive validity for this instrument, the current study attempts to examine this issue on a sample of 4,482 juveniles from Ohio who were given sentences in the community or to juvenile institutions. Results demonstrated the validity of the YLS/CMI in predicting recidivism for both settings.

83 citations

Journal ArticleDOI
TL;DR: In this paper, the authors examined the effectiveness of fifty-eight intensive supervision programs and sought to determine whether program philosophy and treatment integrity are associated with reductions in recidivism, and the results indicated that treatment integrity and program philosophy vary independently of one another.

81 citations

Journal ArticleDOI
TL;DR: In this article, supervision officers were trained in core correctional skills and the risk-need-responsivity (RNR) model, in conjunction with core correctional practices, and the results showed that trained probation officers demonstrated greater use of the skills taught during training and their clients had lower failure rates.

79 citations


Cited by
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Journal ArticleDOI
Cynthia Rudin1
TL;DR: 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 whereinterpretable models could potentially replace black box models in criminal justice, healthcare and computer vision.
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.

3,609 citations

Journal ArticleDOI
TL;DR: GARLAND, 2001, p. 2, the authors argues that a modernidade tardia, esse distintivo padrão de relações sociais, econômicas e culturais, trouxe consigo um conjunto de riscos, inseguranças, and problemas de controle social that deram uma configuração específica às nossas respostas ao crime, ao garantir os altos custos das
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.

2,183 citations

Journal ArticleDOI
14 Apr 2017-Science
TL;DR: This article showed that applying machine learning to ordinary human language results in human-like semantic biases and 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.
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.

1,874 citations

Posted Content
TL;DR: This survey investigated different real-world applications that have shown biases in various ways, and created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems.
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.

1,571 citations

Journal ArticleDOI
01 Jun 2017
TL;DR: It is demonstrated that the criteria cannot all be simultaneously satisfied when recidivism prevalence differs across groups, and how disparate impact can arise when an RPI fails to satisfy the criterion of error rate balance.
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.

1,452 citations