Using Artificial intelligence (AI) or machine learning technology, a team of researchers has predicted outcomes in judicial decisions at the European Court of Human Rights (EctHR) with 79 per cent accuracy. The AI method, developed by researchers from University College London (UCL), University of Sheffield and US-based University of Pennsylvania is the first to predict the outcomes of a major international court by automatically analysing case text using a machine learning algorithm.
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“We don’t see AI replacing judges or lawyers but we think they will find it useful for rapidly identifying patterns in cases that lead to certain outcomes,” said Nikolaos Aletras, who led the study at UCL’s computer science department. “It could also be a valuable tool for highlighting which cases are most likely to be violations of the European Convention on Human Rights,” Aletras added.
In developing the method, 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’. The team along with Daniel Preotiuc-Pietro from University of Pennsylvania extracted case information published by the ECtHR in their publically accessible database.
They identified English language data sets for 584 cases 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.
“This tool would improve efficiencies in courts but to become a reality, we need to test it against more articles and the case data submitted to the court,” noted Lampos in the paper published in the journal PeerJ Computer Science. “The study should be further pursued and refined through the systematic examination of more data,” explained co-author Dimitrios Tsarapatsanis from University of Sheffield.