Greek Scientists in London Invent ‘Judge’ That Can Predict Court Outcome

European Court of Human Rights (ECHR)

Two Greek scientists from the University College of London (UCL) and the University of Sheffield have devised an algorithm that can predict the outcome of complaints filed by applicants to the European Court of Human Rights (ECHR) with 79 percent accuracy. Their invention weighs up legal evidence and moral consideration making the creation of artificial intelligence (AI) a reality.

The new technology could automate the human rights pipeline by analyzing applications and prioritizing them for the ECHR judges. Dr. Nikos Aletras, a UCL computer scientists who co-authored the paper outlining the work said, “It’s important to give priority to cases where there was likely a violation of a person’s human rights.”

Dr. Vasileios Lampos says that the ECHR has a huge pile-up of cases and it would be helpful for the judges to use the algorithm when assessing their legitimacy. The computer will allow them to see the probability of violation of each case.

The computer is fed the facts of cases into a neural database of applicable laws, details regarding the applicants’ countries of origin, and human rights court decisions that have already set precedents for each case. As it turns out, almost every section—from details about the applicant to the bare facts of the complaint—had a similar accuracy rating of around 73 percent. When the AI looked at the court’s run-down of the circumstances surrounding cases, however, that accuracy jumped to 76 percent.

Robots won’t replace judges in the near future, despite the algorithm’s high success rate. “Laws are not structured well enough for a machine to make a decision. I think that judges don’t follow a specific set of rules when making a decision, and I say that as a citizen and computer scientist,” said Dr. Lampos. “Different courts have different interpretations of the same laws, and this happens every day.”

The two Greek scientists are now trying out different types of machines learning on the problem to see if they can get an even higher accuracy rate.