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

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.

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Publication Details

Journal
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Published
2024-10-16
DOI
https://doi.org/10.1609/aies.v7i1.31729
Primary Topic
Law, Economics, and Judicial Systems
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Shomik Jain, Ishana Shastri, Barbara Engelhardt, Ashia Wilson
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Law, Economics, and Judicial Systems
article

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

Shomik Jain, Ishana Shastri, Barbara Engelhardt, Ashia Wilson
article en

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.

Proceedings of the AAAI/ACM Conference on AI Ethics and SocietyVol. 7
Massachusetts Institute of Technology (US), Stanford University (US)
Canadian Institute for Advanced Research
Openalex Percentile: Top 75%
Law, Economics, and Judicial Systems
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