JUSTICE: A Benchmark Dataset for Supreme Court’s Judgment Prediction

CSCI 544 Final Project done by: Mohammed Alsayed, Shaayan Syed, Mohammad Alali, Smit Patel, Hemanth Bodala

Abstract

Artificial intelligence is being utilized in many domains as of late, and the legal system is no exception. However, as it stands now, the number of well-annotated datasets pertaining to legal documents from the Supreme Court of the United States (SCOTUS) is very limited for public use. Even though the Supreme Court rulings are public domain knowledge, trying to do meaningful work with them becomes a much greater task due to the need to manually gather and process that data from scratch each time. Hence, our goal is to create a high-quality dataset of SCOTUS court cases so that they may be readily used in natural language processing (NLP) research and other data-driven applications. Additionally, recent advances in NLP provide us with the tools to build predictive models that can be used to reveal patterns that influence court decisions. By using advanced NLP algorithms to analyze previous court cases, the trained models are able to predict and classify a court's judgment given the case's facts from the plaintiff and the defendant in textual format; in other words, the model is emulating a human jury by generating a final verdict.

Links

arXiv Link: https://arxiv.org/abs/2112.03414

YouTube Link: https://youtu.be/vJ6NQ_UAcVo

Dataset Links:

Minimal JSON compact form (216MB):

https://www.dropbox.com/s/9kyk0dr2gf3ls23/oyez.json?dl=0

Prettified JSON human-readable form (431 MB):

https://www.dropbox.com/s/52a58aac8iujupv/oyez_pretty.json?dl=0