Criminal Justice Forecasts of Risk: A Machine Learning Approach

By Richard A. Berk. 2012

117 citations recorded by the source corpus.

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Citations in the corpus

Fairness in Criminal Justice Risk Assessments: The State of the Art (2017, methodology): “…been ongoing discussions about how much better in practice risk assessments derived from machine learning perform compared to risk assessments derived from older, conventional methods (Berk 2012; Berk and Bleich 2013; Brennan and Oliver 2013; Liu et al. 2011; Rhodes 2013; Ridgeway 2013a, 2013b).”
Algorithmic Decision Making and the Cost of Fairness (2017, background): “Experts increasingly rely on algorithmic decision aids in diverse seings, including law enforcement, education, employment, and medicine [5, 8, 19, 20, 25].”
The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning (2018, background): “In banking, criminal justice, medicine, and beyond, high-stakes decisions are often informed by statistical risk assessments that quantify the likely consequences of potential courses of action (Barocas and Selbst, 2016; Berk, 2012; Shroff, 2017).”
An Experimental Evaluation of the Impact of Intensive Supervision on the Recidivism of High-Risk Probationers (2017, methodology): “These models can be difficult to construct and rely on a vast amount of historical data (Berk, 2012).”
Forecasts of Violence to Inform Sentencing Decisions (2013, methodology): “5 This weighting is broadly consistent with work we have done in the past (Berk 2012).”
Assumption Lean Regression (2018, background): “On the other hand, if the goal is risk assessment in criminal justice (Berk, 2012), the associations reported here may point to predictors that could help improve existing risk assessment instruments.”
Using Regression Kernels to Forecast A Failure to Appear in Court (2014, background): “There is now strong evidence that more accurate forecasts can be obtained directly from machine learning procedures (Berk, 2012; Berk and Bleich, 2013; Ridgeway, 2013; Bushway, 2013) such as random forests (Breiman, 2001), stochastic gradient boosting (Friedman, 2002), Bayesian additive regression…”
Attack Strength vs. Detectability Dilemma in Adversarial Machine Learning (2018, background): “Nontrivial matters are also increasingly entrusted to ML algorithms, such as the justice system determining who gets bail [6] and the Department of Defense investigating their use in national security [7].”
An impact assessment of machine learning risk forecasts on parole board decisions and recidivism (2017, background): “But, the past few years have seen increased scrutiny and controversy surrounding the use of actuarial risk assessments for a variety of criminal justice decisions (Gottfredson and Moriarty 2006; Conroy 2006; Berk 2012; Tonrey 2014; Monahan and Skeem 2014).”
Beyond prediction: Using big data for policy problems (2017, background): “, (2)]; for predicting economic well-being at a granular level using mobile data, satellite imagery, or Google Street View (3, 4, 5); and for allocating fire and health inspectors in cities (6), as well as a variety of other urban appli-”
Statistical Procedures for Forecasting Criminal Behavior (2013, methodology): “This is consistent with published studies (Berk, 2012).”
Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach (2020, background): “This ubiquitous deployment creates an acute need to make AI systems understandable and explainable [5,7,8,12,25].”
Forecasting Domestic Violence: A Machine Learning Approach to Help Inform Arraignment Decisions (2016, background): “There are examples of successful forecasting in other criminal justice settings and for other kinds of crimes (Berk 2012).”