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

TL;DR

This study develops logistic models to analyze the Pennsylvania Additive Classification Tool (PACT), revealing key features and biases in its use for prison security classification and reclassification decisions.

Contribution

It provides the first detailed analysis of PACT's features, biases, and impact, despite limited public information about its internal workings.

Findings

Identifies important features influencing classification decisions

Quantifies biases and disparities in PACT's application

Highlights potential disparate impacts on different groups

Abstract

The Pennsylvania Additive Classification Tool (PACT) is a carceral algorithm used by the Pennsylvania Department of Corrections in order to determine the security level for an incarcerated person in the state's prison system. For a newly incarcerated person it is used in their initial classification. The initial classification can be overridden both for discretionary and administrative reasons. An incarcerated person is reclassified annually using a variant of the PACT and this reclassification can be overridden, too, and for similar reasons. In this paper, for each of these four processes (the two classifications and their corresponding overrides), we develop several logistic models, both binary and multinomial, to replicate these processes with high accuracy. By examining these models, we both 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. Because the details of how the PACT operates have been redacted from public documents, it is important to know how it works and what disparate impact it might have on different incarcerated people.