Forecasting murder within a population of probationers and parolees: a high stakes application of statistical learning

By Richard A. Berk, Lawrence W. Sherman, G. Barnes, E. Kurtz, Lindsay Ahlman. 2009

192 citations recorded by the source corpus.

Subfields: Sociology and Political Science, Statistics and Probability

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Citations in the corpus

Algorithmic Decision Making and the Cost of Fairness (2017, methodology): “In a randomized controlled trial, the Philadel-phia Adult Probation and Parole Department evaluated the effectiveness of a risk assessment tool developed by Berk et al. [10], and found the tool reduced the burden on parolees without significantly increasing rates of re-offense [1].”
Interpretable classification models for recidivism prediction (2015, methodology): “There has been some controversy as to whether sophisticated machine learning methods (such as random forests, Breiman 2001b, Berk et al. 2009, Ritter 2013) are necessary to produce accurate predictive models of recidivism, or if traditional approaches such as logistic regression would sufﬁce (see,…”
An algorithm for removing sensitive information: Application to race-independent recidivism prediction (2017, background): “…of individuals’ future behavior are used to inform judges regarding pre-trial release and bail setting, sentencing, and parole [Phillips et al., 2016, Dieterich et al., 2016, Brennan et al., 2009, Cunningham and Sorensen, 2006, Dvoskin and Heilbrun, 2001, Quinsey et al., 1998, Berk et al., 2009].”
The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables (2017, background): “While prior research has explored applications of machine learning to domains where human judgments are part of the data labeling process (crime [7, 21, 43], medical diagnosis [6, 22]), the selective labels problem is oen overlooked, with evaluation of the models typically carried out by computing ”
Learning to Detect Patterns of Crime (2013, background): “The goal of crime data mining is to understand patterns in criminal behavior in order to predict crime, anticipate criminal activity and prevent it (e.g., see [1]).”
Low-intensity community supervision for low-risk offenders: a randomized, controlled trial (2010, unknown): “Berk et al. (2009) provide a detailed description of the basic statistical techniques used in this revised model to forecast risk.”
Predicting Recidivism in Homicide Offenders Using Classification Tree Analysis (2011, methodology): “…or to test the use of ICT-like techniques on different populations as well as evaluating their performance by comparing them to other actuarial strategies (Silver et al., 2000; Stalans et al., 2004) or establishing prediction error rates using Out Of the Bag (OOB) observations (Berk et al., 2009).”
The Role of Race in Forecasts of Violent Crime (2009, methodology): “Studies of parole or probation outcomes have typically been concerned with any failures, from technical violations to the the most serious crimes (Berk, 2008b; Berk et al., 2009).”
Forecasting Domestic Violence: A Machine Learning Approach to Help Inform Arraignment Decisions (2016, background): “…levels (Berk et al. 2006), homicides committed by probationers or parolees to identify individuals who pose a very serious threat to public safety (Berk et al. 2009), arrests for three different categories of crime committed while on probation or parole to rationalize the intensity of supervision…”
Balancing the Costs of Forecasting Errors in Parole Decisions (2010, methodology): “At least so far, these methods seem to perform far better than conventional modeling approaches (Berk et al., 2009).”
The power few: experimental criminology and the reduction of harm (2007, background): “With over 50,000 active cases at any given time, a 30-year data set showing all clients who are later charged with murder or attempted murder yields an ample haystack in which to find the power few “needles.” Using a nonlinear data mining approach to exploratory analysis, Berk et al. (2007) have d”
Personalized risk assessments in the criminal justice system (2016, methodology): “More recently, Berk et al. (2009) developed a risk assessment tool for the Philadelphia Adult Probation and Parole Department (APPD) to sort offenders into three categories: the highest-risk offenders are considered likely to commit a subsequent violent offense; medium-risk offenders are equally lik”
What You Can and Can’t Properly Do with Regression (2010, background): “Machine learning is also finding its way into the criminology journals (Berk et al. 2009).”
Sacrificing Accuracy for Transparency in Recidivism Risk Assessment: The Impact of Classification Method on Predictive Performance (2016, background): “However, there are quite a few risk-assessment instruments that are deployed with an underlying script from a regression model (see Northpointe, 2015) or a machine learning algorithm (see Berk et al., 2008).”