Pruebas

Accurate and readily available risk determinations play a key role in shaping the structure of the Department and the manner in which individual offenders are supervised. A risk assessment protocol, therefore, is an essential component of the system. To that end, a risk forecasting model, designed by Dr. Richard Berk, was first implemented in 2009 in order to allow for the prediction of offender behavior while under APPD supervision. This model is based upon random forest prediction methods, a specialized classification and regression tree (CART) approach (Berk R. A., 2008). A series of models has been developed for use at APPD in order to reflect a developing capacity for risk stratified supervision and the availability of new data sources (Barnes & Hyatt, 2012). The methodology represents an approach to risk assessment that captures, in addition to traditional measurements of prior conduct, both the measurable and unknown non-linear interactions between predictor variables.

The identification and development of the most accurate and appropriate model for APPD, given limitations on their supervision capabilities, was an iterative process.

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Over a period of several years, a series of prediction models was developed and refined (Barnes & Hyatt, 2012). The prediction model used during this experiment was constructed in late 2009 and was used to assess all cases, at their outset, from April 2010 through November 2011. Each forecast was designed to categorize an offender’s statistically likely conduct for the two years following the start date of the assessment. Although this evaluation focuses on the most serious offenders, the model was designed to classify each case into one of three, mutually exclusive categories necessary for case management:

High Risk: the offender was predicted to commit at least one serious offense (murder, attempted murder, aggravated assault, robbery, or sexual crime) during the first two years of supervision; or

Moderate Risk: the offender was predicted to commit only non-serious offenses during the first two years of supervision; or

Low Risk: the offender was not predicted to commit offenses of any kind, during the first two years of supervision.

The classification of serious and non-serious offending encompasses a majority of the criminal conduct committed in Philadelphia County. The full catalog of offenses was derived from the Pennsylvania State Criminal Code (Title 18), as well as from state administrative law. This list was developed by APPD and JLC researchers to reflect a consensus, both within public policy literature and at the local, political level, regarding the severity of particular offenses. The research team reviewed, on multiple occasions, the developing criminal code and classified over 22,000 individual offenses. It is worth noting that the same classification schema used to determine prediction outcomes was used when classifying participants’ post-random assignment criminal activity for the construction of categorical outcome variables.

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The predictors used in the model reflect data routinely and electronically available at intake, and include criminal history, prior sentences, and demographic information. Berk et al. (2009) provides a comprehensive explanation of the statistical techniques used to forecast risk during the RCT, while an inclusive description of the model, including the predictor variables used, accuracy and cost ratios, can be found in Barnes and Hyatt (2012). A summary of the variables included in the prediction process during the course of this experiment is included in Appendix D. The data used to make forecasts, including measures of demographic characteristics, criminal history and prior conduct on supervision, are all available in machine-readable format and were collected as part of standard, administrative processes within the local and state court systems.

The risk forecasting model and the computer programs needed to make live predictions were integrated into the APPD intake department as part of the procedures used to manage all incoming cases of probation. This allowed the intake department to complete multiple actions simultaneously, all of which were necessary for both risk- based supervision and this randomized trial. Notably, this system allowed for the automation of the intake process, minimizing the opportunities for error and allowing for the blinding necessary during the experiment.

In practice, when an offender was sentenced directly to probation, they were in most cases given a paper copy of the judicial order and told to report to directly to the Intake Department. Located on the lowest levels of the Courthouse, the APPD staff in the office was responsible for entering the criminal case and sentence information into the Department’s internal case tracking system (“Monitor”). Using the JLC program, these staff needed only enter the docket number of the case and the offender’s Police

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Photo Number (PPN).10 The computer program then gathered all of the data necessary for the generation of a risk forecast, ran this information though the random forest prediction model, retrieved the prediction and assigned the offender to the appropriate officer (Barnes & Hyatt, 2012). During the experiment, this same set of programs, after retrieving the risk score, conducted eligibility checks, randomized participants and, where necessary, blinded the assessor and officer as to the “true” risk score.

This system, with the exception of the random assignment process, remains in effect at APPD and is used to forecast all incoming cases.