Sunday, 22 January 2017

Computer algorithm predicts most Strasbourg judgments

Computer algorithm predicts most Strasbourg judgments

Artificial intelligence … it’s no longer in the future. It’s with us now.

I posted a review of a book about artificial intelligence in autumn last year. The author’s argument was not that we might find ourselves, some time in the future, subservient to or even enslaved by cool-looking androids from Westworld. His thesis is more disturbing: it’s happening now, and it’s not robots. We are handing over our autonomy to a set of computer instructions called algorithms.

If you remember from my post on that book, I picked out a paragraph that should give pause to any parent urging their offspring to run the gamut of law-school, training contract, pupillage and the never never land of equity partnership or tenancy in today’s competitive legal industry. Yuval Noah Harari suggests that the everything lawyers do now – from the management of company mergers and acquisitions, to deciding on intentionality in negligence or criminal cases – can and will be performed a hundred times more efficiently by computers.

Now here is proof of concept. University College London has just announced the results of the project it gave to its AI researchers, working with a team from the universities of Sheffield and Pennsylvania. Its news website announces that a machine learning algorithm has just analysed, and predicted, “the outcomes of a major international court”:

    The judicial decisions of the European Court of Human Rights (ECtHR) have been predicted to 79% accuracy using an artificial intelligence (AI) method.

Nicolas Aletras, the computer scientist who led the project, reassures us that they “don’t see AI replacing judges or lawyers”. This study, he suggests, will help the legal industry that has grown up around the Strasbourg Court to identify “patterns in cases that lead to certain outcomes”. Indeed the result of the study bears out a prediction made over fifty years ago that computers would one day become able to analyze and predict the outcomes of judicial decisions (Lawlor, 1963). According to Lawlor,

    reliable prediction of the activity of judges would depend on a scientific understanding of the ways that the law and the facts impact on the relevant decision-makers, i.e., the judges.

Now we have significant advances in two types of processing,  Natural Language Processing (NLP) and Machine Learning (ML), and the authors argue that these provide us with the tools to automatically analyze legal materials, so as to build successful predictive models of judicial outcomes.

What is there not to like? Well, one thing is that if an algorithm can identify these patterns, why do we need people to do it when an AI machine can manage the same task a hundred times faster at a fraction of the cost?

But the question goes deeper. The researchers looked closely at applications under Articles 3 (prevention of inhuman treatment), 6 (right to justice) and 8 (right to bodily integrity and family life). They then instructed the machine to apply a pattern to the text of the judgments. Here’s a quick reminder of how the ECHR judgments present themselves:

    circumstances (which is the factual/legal matrix of the individual case)
     relevant law
    ‘topics” covered in the discussion
    Court’s assessment of the law and facts
    judgment of the Court

As the co-author of this study explains, the machine learned how to combine the abstract ‘topics” (such as the right to privacy, or the incidence of negligence) with the “circumstances” section across the 584 cases it was given to chew over.

According to the project’s co-author, Dr Vasileios Lampos of UCL Computer Science, 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.

    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.

In fact in this instance the research team were hobbled by data protection and privacy laws: they were not allowed to look at the  applications that were actually submitted to the court. All they had to go on were the published judgments.

In other words, the AI machine achieved a staggeringly high prediction level of judicial outcomes, with hardly any data to go on. The text in the judgments could be seen as a proxy for the applications actually lodged at the Court by individuals. The authors point out that at the very least, their work could be approached on the following hypothetical basis:

    if there is enough similarity between the chunks of text of published judgments that we analyzed and that of lodged applications and briefs, then our approach can be fruitfully used to predict outcomes with these other kinds of texts.

There could be sufficient similarity, simply because in the vast majority of cases, parties do not tend to dispute the facts themselves, as contained in the ‘Circumstances’ subsection, but only their legal significance (i.e., whether a violation took place or not, given those facts).  If the research team had been given access to the actual complaints submitted to the court, the calculations as to the outcome would have been closer to perfect.

In the abstract to the full paper, the authors reflect that their empirical analysis indicates that the formal facts of a case are the most important predictive factor. This is consistent with the theory of legal realism suggesting that judicial decision-making is significantly affected by the stimulus of the facts,and in the body of the paper, they observe that :

    The consistently more robust predictive accuracy of the ‘Circumstances’ subsection suggests a strong correlation between the facts of a case, as these are formulated by the Court in this subsection, and the decisions made by judges. The relatively lower predictive accuracy of the ‘Law’ subsection could also be an indicator of the fact that legal reasons and arguments of a case have a weaker correlation with decisions made by the Court.

In other words it is facts, rather than the law, that are predictive of the judicial outcome. If a computer can work this out one might be forgiven for wondering whether cases should wind their labyrinthine way through lawyers’ wet brains to a panel of judges at the other end.

But that is a place beyond the dark horizon. All the UCL team was doing was running a controlled experiment on one court, in a familiar legal environment, using tools that could readily latch on to listed arguments (all those associated with Articles 2 – 14 of the ECHR and its relevant protocols)

Soon we might expect this sort of tool to provide every service from in-house legal advice to final adjudication. Why not?




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