Automating Transparency Mechanisms in the Judicial System Using LLMs: Opportunities and Challenges

TL;DR

This paper explores how large language models can automate transparency efforts in the judicial system, focusing on jury selection and eviction cases, highlighting potential benefits and challenges.

Contribution

It introduces the application of LLMs to automate transparency in judicial processes, addressing opportunities and challenges in two key areas.

Findings

LLMs can extract information from unstructured legal documents.

Potential to reduce manual effort in transparency audits.

Challenges include ensuring accuracy and addressing bias.

Abstract

Bringing more transparency to the judicial system for the purposes of increasing accountability often demands extensive effort from auditors who must meticulously sift through numerous disorganized legal case files to detect patterns of bias and errors. For example, the high-profile investigation into the Curtis Flowers case took seven reporters a full year to assemble evidence about the prosecutor's history of selecting racially biased juries. LLMs have the potential to automate and scale these transparency pipelines, especially given their demonstrated capabilities to extract information from unstructured documents. We discuss the opportunities and challenges of using LLMs to provide transparency in two important court processes: jury selection in criminal trials and housing eviction cases.