Reprogramming Recidivism: The First Step Act and Algorithmic Prediction of Risk

Abstract

The First Step Act, a seemingly miraculous bipartisan criminal justice reform bill, was signed into law in late 2018. The Act directed the Attorney General to develop a risk and needs assessment tool that would effectively determine who would be eligible for early release based on an algorithmic prediction of recidivism. The resulting tool—PATTERN—was released in the summer of 2019 and quickly updated in January of 2020. It was immediately put to use in an unexpected manner, helping to determine who was eligible for early release during the COVID-19 pandemic. It is now the latest in a growing list of algorithmic recidivism prediction tools, tools that first came to mainstream notice with critical reporting about the COMPAS sentencing algorithm.

This Article evaluates PATTERN, both in its development as well as its still-evolving implementation. In some ways, the PATTERN algorithm represents tentative steps in the right direction on issues like transparency, public input, and use of dynamic factors. But PATTERN, like many algorithmic decision-making tools, will have a disproportionate impact on Black inmates; it provides fewer opportunities for inmates to reduce their risk score than it claims and is still shrouded in some secrecy due to the government’s decision to dismiss repeated calls to release more information about it. Perhaps most perplexing, it is unclear whether the tool actually advances accuracy with its predictions. This Article concludes that PATTERN is a decent first step, but it still has a long way to go before it is truly reformative.

Keywords: First Step Act, recidivism, algorithmic prediction, PATTERN, COVID-19, criminal justice, reform, risk assessment, machine learning, department of justice, transparency, racial disparity,

JEL Classification: K14