High-Level Simulation Model of a Criminal Justice System

Dabbaghian, Vahid; Jula, Payman; Borwein, Peter; Fowler, Ellen; Giles, Christopher; Richardson, Nicholas; Rutherford, Alexander R.; Waall, Alexa van der

doi:10.1007/978-3-642-39149-1_6

Cited by 1 publication

(1 citation statement)

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“…Blumstein [1] used this approach to model complete criminal justice systems and to understand which variables most influenced the cost and the flow of offenders through the system. A more recent study can be found in [3]. Moranian et al [13] carried out a simulation study for juvenile courts to determine which variables influenced the rate of flow most strongly.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty in Criminal Justice Algorithms: simulation studies of the Pennsylvania Additive Classification Tool

Dhar¹,

Massaro²,

Mir³

et al. 2021

Preprint

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Much attention has been paid to algorithms related to sentencing, the setting of bail, parole decisions and recidivism while less attention has been paid to carceral algorithms, those algorithms used to determine an incarcerated individual's lived experience. In this paper we study one such algorithm, the Pennsylvania Additive Classification Tool (PACT) that assigns custody levels to incarcerated individuals. We analyze the PACT in ways that criminal justice algorithms are often analyzed: namely, we train an accurate machine learning model for the PACT; we study its fairness across sex, age and race; and we determine which features are most important. In addition to these conventional computations, we propose and carry out some new ways to study such algorithms. Instead of focusing on the outcomes themselves, we propose shifting our attention to the variability in the outcomes, especially because many carceral algorithms are used repeatedly and there can be a propagation of uncertainty. By carrying out several simulations of assigning custody levels, we shine light on problematic aspects of tools like the PACT.

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Section: Introductionmentioning

confidence: 99%