Forecasts of Violence to Inform Sentencing Decisions

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Interpretable classification models for recidivism prediction (2015, methodology): “The Radial Basis Function (RBF) kernel allows the method to handle classiﬁcation problems where the decision-boundary may be non-linear (see, e.g., Cristianini and Shawe-Taylor 2000, Berk and Bleich 2014).”
Nber Working Paper Series Human Decisions and Machine Predictions Human Decisions and Machine Predictions (2017, background): “For example Richard Berk and his colleagues have shown risk to be predictable in diverse criminal justice contexts; see for example the summary in Berk (2012) and subsequent results in Berk and Bleich (2013) and Berk et al. (2014). It merely means that we cannot”
Forecasting Domestic Violence: A Machine Learning Approach to Help Inform Arraignment Decisions (2016, background): “…three different categories of crime committed while on probation or parole to rationalize the intensity of supervision and the kinds of services offered (Berk et al. 2010), and future violence to help judges make post-conviction sentencing decisions (Berk & Bleich 2013, 2014; Berk & Hyatt 2015).”
Proxy Non-Discrimination in Data-Driven Systems (2017, background): “Also, these models represent a significant fraction of models used in practice in predictive systems that operate on personal information, ranging from advertising [13], psychopathy [31], criminal justice [7, 8], and actuarial sciences [27, 29].”