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.

Empirical Evidence on the Importance of Training and Experience in Using the Level of Service Inventory-Revised

Abstract: Recent trends in corrections have mandated the adoption and use of risk and need assessments for offenders. Research indicates that many correctional agencies around the country either currently use or are in the process of implementing risk and need assessment instruments. One example of an instrument that is being implemented on a wide scale is the Level of Service Inventory–Revised (LSI-R). Data from Multi-Health Systems, Inc. (MHS), the company that markets the LSI-R, indicate that more than 600 agencies in United States currently use this risk/need assessment tool. While increasing use of objective classification instruments is encouraging, simultaneously there are growing concerns regarding the effective implementation of these “third-generation” risk/need assessment tools. The authors of the LSI-R, in discussing risk assessment in general, express concern about the ability of correctional practitioners to understand and properly administer risk assessment instruments. Research by Bonta, Bogue, Crowley, and Motiuk (2001) that investigated the implementation of the LSI-R indicated error rates that were of concern. Their research also indicated that these error rates could be reduced through the use of training prior to implementation. While currently there is a lack of quality assurance research conducted on other instruments such as the Wisconsin Risk and Need scales or the Salient Factor Score, it is conceivable that errors occur in these instruments as well. This may be particularly true when considering the potential for agencies, which may be in a rush to implement best practices, to skip necessary, preliminary research and training.

Implementing risk assessment in the federal pretrial services system.

Abstract: IN 2009 THE ADMINISTRATIVE Office of the U.S. Courts and the Office of Federal Detention Trustee (a Justice Department agency charged with administering and controlling the costs of pretrial detention in the federal system) published Pretrial Risk Assessment in the Federal Court, which recommended that the federal pretrial services system develop and implement an actuarial risk assessment tool. Ever since then, the system has been moving towards that goal. Lowenkamp and Whetzel (2009) have already detailed the process followed and described the tool that was ultimately developed, the Pretrial Risk Assessment (PTRA). As of August 2011 the tool had been fully implemented in almost all districts; therefore, it seems an appropriate time to assess the tool in light of the available pretrial risk assessment literature, determine how implementation has proceeded in the federal system to this point, and assess the ultimate impact, if any, of the tool on the federal pretrial services system. Perhaps the most important question is whether the tool has begun to affect officer recommendations for release/detention and/or release rates in districts where it has been operational for a year or more.

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 1

Abstract: Alexander M. Holsinger Criminal Justice Coordinator, Johnson County, KS Christopher T. Lowenkamp Administrative Office of the U.S. Courts Edward J. Latessa University of Cincinnati Ralph Serin Carleton University, Ottawa Thomas H. Cohen Administrative Office of the U.S. Courts Charles R. Robinson Administrative Office of the U.S. Courts Anthony W. Flores California State University, Bakersfield Scott W. VanBenschoten Administrative Office of the U.S. Courts

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.