• Corpus ID: 244773123

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

@article{Dhar2021UncertaintyIC,
  title={Uncertainty in Criminal Justice Algorithms: simulation studies of the Pennsylvania Additive Classification Tool},
  author={Swarup Dhar and Vanessa A. Massaro and Darakhshan J. Mir and Nathan C. Ryan},
  journal={ArXiv},
  year={2021},
  volume={abs/2112.00301}
}
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… 

Estimating and Controlling for Fairness via Sensitive Attribute Predictors

TLDR
This work demonstrates that in settings where sensitive attributes are unknown, one can still reliably estimate and ultimately control for fairness by using proxy sensitive attributes derived from a sensitive attribute predictor.

Carceral algorithms and the history of control: An analysis of the Pennsylvania additive classification tool

Scholars have focused on algorithms used during sentencing, bail, and parole, but little work explores what we term “carceral algorithms” that are used during incarceration. This paper is focused on

Analysis of the Pennsylvania Additive Classification Tool: Biases and Important Features

TLDR
Several logistic models are developed to identify which features are most important in the model and quantify and describe biases that exist in the PACT, its overrides, and its use in reclassification.

References

SHOWING 1-10 OF 21 REFERENCES

Algorithmic Fairness: Choices, Assumptions, and Definitions

TLDR
It is shown how choices and assumptions made—often implicitly—to justify the use of prediction-based decision-making can raise fairness concerns and a notationally consistent catalog of fairness definitions from the literature is presented.

FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems

TLDR
An open source Python package called FAT Forensics, which can inspect important fairness, accountability and transparency aspects of predictive algorithms to automatically and objectively report them back to engineers and users of such systems.

To predict and serve?

Predictive policing systems are used increasingly by law enforcement to try to prevent crime before it occurs. But what happens when these systems are trained using biased data? Kristian Lum and

Carceral Spaces: Mobility and Agency in Imprisonment and Migrant Detention

Contents: Introduction, Dominique Moran, Deirdre Conlon and Nick Gill Part I Mobility: On mobilities and migrations, Alison Mountz Mobility versus liberty? The punitive uses of movement within and

A Simulation Based Dynamic Evaluation Framework for System-wide Algorithmic Fairness

TLDR
The introduction of agent based simulation techniques will strengthen collaboration with social scientists, arriving at a better understanding of the social systems affected by technology and to hopefully lead to concrete policy proposals that can be presented to policymakers for a true systemic transformation.

High-Level Simulation Model of a Criminal Justice System

TLDR
A system dynamics model of the criminal justice system of the British Columbia, Canada is developed, and it is shown how this model can assist strategic decision-makers and managers make better decisions.

Models of a Total Criminal Justice System

TLDR
This paper formulates a model for the criminal justice system in one state; it depicts the flow of arrested persons through the system as a function of type of crime, and provides a basis for apportioning costs to system components and to types of crime.

Runaway Feedback Loops in Predictive Policing

TLDR
A mathematical model of predictive policing is developed that proves why this feedback loop occurs, shows empirically that this model exhibits such problems, and demonstrates how to change the inputs to a predictive policing system so the runaway feedback loop does not occur, allowing the true crime rate to be learned.

Counterfactual Fairness

TLDR
This paper develops a framework for modeling fairness using tools from causal inference and demonstrates the framework on a real-world problem of fair prediction of success in law school.

Simulation in criminal justice: A case study of the juvenile court system

TLDR
The purpose of this paper is to study the flow of delinquents through the juvenile court system in order to examine those elements which control the rate of flow and trade-offs between allocated court resources and gains in flow time can be evaluated.