Analyzing a Carceral Algorithm used by the Pennsylvania Department of Corrections

TL;DR

This paper critically examines the Pennsylvania Additive Classification Tool (PACT), revealing its role in perpetuating racial bias and control within incarceration, and warns against uncritical reliance on such data-driven algorithms in criminal justice reform.

Contribution

It provides a novel analysis of an incarceration algorithm, highlighting its bias, historical context, and implications for prisoner treatment and reform policies.

Findings

PACT is rooted in control and securitization rather than rehabilitation.

The algorithm exhibits racial bias and contains errors and omissions.

Data-driven reforms risk reinforcing existing biases and inaccuracies.

Abstract

Scholars have focused on algorithms used during sentencing, bail, and parole, but little work explores what we call carceral algorithms that are used during incarceration. This paper is focused on the Pennsylvania Additive Classification Tool (PACT) used to classify prisoners' custody levels while they are incarcerated. Algorithms that are used during incarceration warrant deeper attention by scholars because they have the power to enact the lived reality of the prisoner. The algorithm in this case determines the likelihood a person would endure additional disciplinary actions, can complete required programming, and gain experiences that, among other things, are distilled into variables feeding into the parole algorithm. Given such power, examining algorithms used on people currently incarcerated offers a unique analytic view to think about the dialectic relationship between data and algorithms. Our examination of the PACT is two-fold and complementary. First, our qualitative overview of the historical context surrounding PACT reveals that it is designed to prioritize incapacitation and control over rehabilitation. While it closely informs prisoner rehabilitation plans and parole considerations, it is rooted in population management for prison securitization. Second, on analyzing data for 146,793 incarcerated people in PA, along with associated metadata related to the PACT, we find it is replete with racial bias as well as errors, omissions, and inaccuracies. Our findings to date further caution against data-driven criminal justice reforms that rely on pre-existing data infrastructures and expansive, uncritical, data-collection routines.