HAI Policy Briefs
A New Direction for Machine Learning in Criminal Law
The American criminal legal system is rife with—and perpetuates—inequality. These discrimination problems across racial, socioeconomic, and other lines are well-documented, but studying the problem is still a resource-intensive process. Technology may be able to relieve some of this burden. In this brief, we propose using machine learning to analyze decision-making in the criminal legal system. The aim is not to predict human behavior or replace human decision-making, but to better understand the factors that led to past decisions in the hopes of facilitating increased fairness and consistency in how criminal law is applied. We call it the “Recon Approach.”
➜ Using machine learning to analyze decision-making in the criminal legal system could be a valuable way to identify discrimination and facilitate reconsideration of decisions where justice was inconsistently applied—but reconsideration is still a decision, and stakeholders in criminal law processes should consider whether and how machine learning should play a role in that decision.
➜ We propose a two-pronged “Recon Approach” to use machine learning in criminal law: reconnaissance, where machine learning identifies patterns in a human decision-making process, and reconsideration, where machine learning then focuses on individual decision cases.
➜ The Recon Approach is meant for scenarios where humans make discretionary decisions, there are records which describe decision factors, and those records are analyzable by a machine learning tool (e.g., a hearing transcript readable by a language-processing algorithm).
➜ Policymakers should consider implementing stronger, clearer public record laws to ensure researchers have access to the necessary data to conduct these reviews.
Kristen Bell - University of Oregon School of Law
Jenny Hong - Stanford University
Nick McKeown - Stanford University
Catalin Voss - Stanford University