Scientists have developed a new artificial intelligence (AI) system that can accurately predict the outcomes of human right trials in major international courts - an advance that could be a valuable tool to identify violation of rights.
Using the new AI method, judicial decisions of cases at the European Court of Human Rights (ECtHR) have been predicted to 79 per cent accuracy.
The method is the first to predict the outcomes of a major international court by automatically analysing case text using a machine learning algorithm.
“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,” said Nikolaos Aletras from University College London, who led the study.
“It could also be a valuable tool for highlighting which cases are most likely to be violations of the European Convention on Human Rights,” said Aletras.
The team found that judgements by the ECtHR are highly correlated to non-legal facts rather than directly legal arguments, suggesting that judges of the court are ‘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 study, which is the first of its kind, corroborates the findings of other empirical work on the determinants of reasoning performed by high level courts,” said Dimitrios Tsarapatsanis, from the University of Sheffield.
Researchers extracted case information published by the ECtHR in their publicly accessible database.
They identified English language data sets for 584 cases relating to torture and inhuman and degrading treatment, right to a fair trial and right to respect for one’s “private and family life, his home and his correspondence”.
Researchers applied an AI algorithm to find patterns in the text. To prevent bias and mislearning, they selected an equal number of violation and non-violation cases.
The most reliable factors for predicting the court’s decision were found to be the language used as well as the topics and circumstances mentioned in the case text.
The ‘circumstances’ section of the text includes information about the factual background to the case.
By combining the information extracted from the abstract ‘topics’ that the cases cover and ‘circumstances’ across data for all three articles, an accuracy of 79 per cent was achieved.
“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,” said Lampos.
The study was published in the journal PeerJ Computer Science.