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license: apache-2.0 |
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Predict the judgment of a court using NLP |
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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 |
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The dataset contains 3304 cases from the Supreme Court of the United States from 1955 to 2021. Each case has the case's identifiers as well as the facts of the case and the decision outcome. Other related datasets rarely included the facts of the case which could prove to be helpful in natural language processing applications. One potential use case of this dataset is determining the outcome of a case using its facts. |
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Target Variable: First Party Winner, if true means that the first party won, and if false it means that the second party won. |
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Use NLP techniques to build features out of facts column. |
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Ask question like "In 1970 Texas abortion case, did the first party win?" |
