Leveraging AI for Plain Language Summaries of Court Documents

Abstract

Court documents provide invaluable insights into the workings of our judicial system. However, their complexity, often stemming from dense legal terminology, can make them challenging to understand. Moreover, summarizing multiple documents is a time-intensive task for legal professionals. As part of Dr. Charlotte Alexander’s Law, Data, and Design Lab, Thomas Orth, an OMSCS student, has been collaborating with graduate and undergraduate students to develop an AI-driven solution for summarizing court documents using Large Language Models (LLMs). In this talk, Thomas will discuss the lab’s partnership with the Civil Rights Litigation Clearinghouse, the types of documents being analyzed, and their innovative approach to AI-driven summarization

Bio

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Thomas Orth is a Senior AI Research Engineer for Lockheed Martin where he works on Computer Vision and Generative AI problems across the corporations. Thomas also is a research student, focused on the intersectionality of Law and Generative AI.