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A Computational Intelligence Model for Legal Prediction and Decision Support.

Xuerui Shang1

  • 1School of Law, Shanghai University of Finance and Economics, Shanghai, 200433, China.

Computational Intelligence and Neuroscience
|July 5, 2022
PubMed
Summary

This study introduces a novel legal judgment prediction model using process supervision to improve AI-driven legal decision support. The model enhances accuracy in predicting case verdicts by effectively managing sequential task dependencies.

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Area of Science:

  • Artificial Intelligence
  • Computational Law
  • Machine Learning

Background:

  • Legal judgment prediction (LJP) aims to automate case verdict forecasting using AI.
  • Existing methods often struggle with the sequential nature of legal subtasks.
  • Decision support systems can enhance efficiency for legal professionals.

Purpose of the Study:

  • Propose a novel legal judgment prediction model based on process supervision.
  • Address the sequential dependencies inherent in legal judgment prediction tasks.
  • Improve the accuracy and efficiency of AI in the legal field.

Main Methods:

  • Utilized Convolutional Neural Network (CNN) for text feature extraction.
  • Applied Principal Component Analysis (PCA) for data feature dimensionality reduction.
  • Introduced process supervision to model sequential subtask dependencies accurately.
  • Employed Genetic Algorithm (GA) for parameter optimization.

Main Results:

  • The proposed model framework and process monitoring mechanism proved effective.
  • Achieved superior performance compared to benchmark methods on four legal datasets (CAIL2018/2019 Small/Large).
  • Demonstrated the efficacy of process supervision in capturing dependency information.

Conclusions:

  • The developed legal judgment prediction model significantly enhances prediction accuracy.
  • Automatic legal judgment prediction can assist legal professionals and provide public legal aid.
  • Process supervision is a viable mechanism for improving AI in sequential legal tasks.