An artificial intelligence "judge" that can accurately predict many of Europe's top human rights court rulings has been created by a team of computer scientists and legal experts.

The AI system—developed by researchers from University College London, the University of Sheffield, and the University of Pennsylvania—parsed 584 cases which had previously been heard at the European Court of Human Rights (ECHR), and successfully predicted 79 percent of the decisions.

A machine learning algorithm was trained to search for patterns in English-language datasets relating to three articles of the European Convention on Human Rights: Article 3, concerning torture and inhuman and degrading treatment; Article 6, which protects the right to fair trial; and Article 8, on the right to a private and family life. The cases examined were equally split between those that did find rights violations and those that didn't.

Despite the AI's success, the legal profession is safe for now. UCL computer scientist Nikolaos Aletras, who led the study, said:

We don’t see AI replacing judges or lawyers, but we think they’d find it useful for rapidly identifying patterns in cases that lead to certain outcomes. It could also be a valuable tool for highlighting which cases are most likely to be violations of the European Convention on Human Rights.

Researchers found that ECHR judgments "are highly correlated to non-legal facts rather than directly legal arguments, suggesting that judges of the Court are, in the jargon of legal theory, 'realists' rather than 'formalists'. This supports findings from previous studies of the decision-making processes of other high level courts, including the US Supreme Court."

The best apparent predictors of the court's decision in the case text were the language used, as well as topics and circumstances. The AI worked by comparing the facts of the circumstances with the more abstract topics covered by the cases.

"Previous studies have predicted outcomes based on the nature of the crime, or the policy position of each judge, so this is the first time judgments have been predicted using analysis of text prepared by the court," said UCL's Vasileios Lampos.

"We expect this sort of tool would improve efficiencies of high-level, in-demand courts, but to become a reality, we need to test it against more articles and the case data submitted to the court."