Algorithm correctly predicts outcome of 79 per cent of human rights cases

Dr Nikolaos Alestras

University researchers have developed an algorithm that can predict the outcome of cases in the European Court of Human Rights (ECtHR) with 79 per cent accuracy.

Researchers at University College London, the University of Sheffield and the University of Pennsylvania used a machine learning algorithm to analyse the text of 584 cases relating to articles 3, 6 and 8 of the European Convention on Human Rights.

The UK team, alongside Dr Daniel Preoţiuc-Pietro from the University of Pennsylvania, extracted case information published by the ECtHR in their publically accessible database.

To prevent bias and mislearning, they selected an equal number of violation and non-violation cases.

Dr Nikolaos Alestras, who led the study at UCL Computer Science, 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.”

In developing the method, the team found that ECtHR judgments are highly correlated to non-legal facts rather than directly legal arguments, suggesting that judges of the Court are legal realists rather than formalists.

The most reliable factors for predictions were the language used as well as the topics and circumstances mentioned in the case text. By combining them, an accuracy of 79 per cent was achieved.

research paper has been published in PeerJ Computer Science.

Dr Vasileios Lampos, co-author of the paper, said: “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 judgements have been predicted using analysis of text prepared by the court.

“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.”