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This study developed a system to predict if Dutch court decisions will be cited, helping legal professionals find authoritative case law more easily. Explainable models using metadata performed nearly as well as complex text-based systems.

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Area of Science:

  • Legal informatics
  • Computational law
  • Artificial intelligence in law

Background:

  • Increasing online accessibility of case law presents challenges in identifying relevant legal precedents.
  • The planned expansion of online case law publication in the Netherlands will intensify this issue.
  • Distinguishing authoritative case law from less influential decisions is crucial for legal research efficiency.

Purpose of the Study:

  • To develop a predictive model for identifying cited court decisions, thereby distinguishing authoritative cases.
  • To aid legal practitioners and scholars in efficiently navigating large volumes of legal data.
  • To reduce the time and effort required for legal case preparation and analysis.

Main Methods:

  • Predictive modeling to forecast citation of court decisions post-publication.
  • Utilizing handcrafted metadata features and full text analysis for model development.
  • Evaluating model performance using the Matthews Correlation Coefficient (MCC).

Main Results:

  • The predictive model showed a strong correlation for the Dutch Supreme Court (MCC = 0.60).
  • Performance was less successful for the Council of State (MCC = 0.26) and district courts (MCC = 0.17).
  • Explainable models using only metadata features achieved performance comparable to less interpretable text-based models.

Conclusions:

  • Predictive citation analysis offers a viable method for filtering authoritative legal decisions.
  • Metadata-driven, explainable AI models provide a promising approach for legal informatics applications.
  • Further refinement is needed to improve prediction accuracy across different court levels.