Using machine learning to predict decisions of the European Court of Human Rights

Masha Medvedeva*, Michel Vols, Martijn Wieling

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

18 Citations (Scopus)
149 Downloads (Pure)

Abstract

When courts started publishing judgements, big data analysis (i.e. large-scale statistical analysis of case law and machine learning) within the legal domain became possible. By taking data from the European Court of Human Rights as an example, we investigate how natural language processing tools can be used to analyse texts of the court proceedings in order to automatically predict (future) judicial decisions. With an average accuracy of 75% in predicting the violation of 9 articles of the European Convention on Human Rights our (relatively simple) approach highlights the potential of machine learning approaches in the legal domain. We show, however, that predicting decisions for future cases based on the cases from the past negatively impacts performance (average accuracy range from 58 to 68%). Furthermore, we demonstrate that we can achieve a relatively high classification performance (average accuracy of 65%) when predicting outcomes based only on the surnames of the judges that try the case.

Original languageEnglish
Pages (from-to)237-266
Number of pages30
JournalArtificial Intelligence and Law
Volume28
Issue number2
Early online date26-Jun-2019
DOIs
Publication statusPublished - Jun-2020

Keywords

  • Machine learning
  • Case law
  • European Court of Human Rights
  • Natural language processing
  • Judicial decisions
  • NETWORK ANALYSIS
  • LAW
  • CITATIONS
  • JUDICIARY
  • BEHAVIOR
  • JUDGES

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