Novel Deep Learning Approach for Court Case Outcome Prediction, Explanation, and Strategy Suggestion

Abstract

"Accurately predicting court case outcomes is difficult and unclear with traditional methods. This research proposes using advanced deep learning models to solve this challenge. These models would not only predict outcomes, but also explain their reasoning and suggest actions to improve case success. This research aims to create a more informed and strategic approach to litigation. This study introduces a novel approach for predicting the outcome of court cases by employing an analytical model trained on past case data. This model is then applied to current case text, aiding legal professionals in preparing their arguments more effectively for better chances of success. To enhance model interpretability, domain-specific sub-models and various model variations are incorporated. The process involves passing the court case document's sentence vector sequence to a RoBERTa model. The output of this model is then used to determine the petitioner party's likelihood of winning via a classifier component.. It's noted that in legal cases, the likelihood of the defendant winning is often considered equivalent to the petitioner losing, though exceptions may exist. This principle guides the research approach. This research further proposes incorporating the Gemma 7B large language model to improve the deep learning model's ability to explain its reasoning and suggest legal strategies. These language models would analyze factors influencing case outcomes and offer tailored recommendations to increase litigation success. The Winning Case Prediction model has active 74.8 accuracy and Reasoning generating model has an active 1.77 loss and Recommendation generating model has active 0.0151 loss which surpasses other models tested."