The law is a promising area for AI
The legal profession is rarely accused of being at the cutting edge of technological development. Lawyers may not still use quill pens, but they’re not exactly famous for their IT skills. Nevertheless, it has a number of characteristics which make it eminently suited to the deployment of advanced AI systems. Lawyers are deluged by data, and commercial law cases can be highly lucrative.
One man who knows more about this than most is Benjamin Alarie, a Professor at the University of Toronto Faculty of Law, and a successful entrepreneur. In 2015, he co-founded Blue J, a Toronto-based company which uses machine learning to analyse large amounts of data to predict a court’s likely verdict in legal cases. It is used by the Department of Justice in Canada and Canada’s Revenue Agency.
Alarie has just published “The Legal Singularity: How Artificial Intelligence Can Make Law Radically Better.” He joined the London Futurists Podcast to discuss the future of AI in the legal profession.
Automation
One way in which AI is impacting the law is automation. Traditionally, a lot of legal work was repetitive and robotic. Junior lawyers working on transactions and on litigation spent days cooped up in “deal rooms”, working their weary way through piles of boxes, looking for the word or phrase in a document which could undermine a deal, or clinch a lawsuit. This is known as “discovery” in the US and “disclosure” in the UK. Machines excel at the close analysis of huge volumes of text, and much of this work has already been automated.
For instance, a human lawyer would take days to identify and summarise the change-of-control provisions in multiple commercial leases. A machine can do the same work in seconds. Law firms have not become less profitable since this work was automated by machines, so the firms are probably doing a great deal more of it, but at lower unit cost. They have learned how to sell the capabilities of their machines instead of simply selling the time of their junior employees.
Predictions
Lawyers make a lot of predictions. They are forever second-guessing each other, and also the judges who will decide their cases. Their advice to clients is based on these predictions. Deep learning and generative AIs are prediction machines, so they should be extremely helpful to lawyers.
When judges make decisions, they take into account all the evidence that is formally presented. But they also take into account the way the parties present themselves – their dress, their accents, their posture, their gestures, and their facial expressions. They may try to downplay some of this information, in order to avoid being biased or prejudiced. But as humans, they cannot help but be influenced by it. Indeed it is often a critical part of their job to make judgments about the honesty of a defendant or a witness, and about their ability to observe and explain the circumstances of a case.
Machines do not have that human ability to assess the disposition or capability of a party in a case. What they do have is the ability to remember every detail about the hundreds of thousands of prior cases which could be relevant to a particular decision. This is what enables Alarie’s company Blue J to predict the outcome of a particular case with over 90% accuracy. This is impressive: large amounts of money depend on the judgment whether or not to proceed with a case, so traditionally, it is the job of the most senior lawyers to predict the likely outcome of a case, and to advise a client whether or not to go to court.
Centaurs
As is so often the case with AI at this stage of its development, the ideal situation is to combine the capabilities of the machine and the human lawyer. The pairing of the two is often compared with the mythical centaur, which was half-man and half-horse. In this case the combination is the machine’s comprehensive knowledge of past cases, and the human’s ability to assess other people. The interesting thing, though, is that the machines are quickly getting much better, and the humans are not. And humans don’t scale, whereas machines do.
As AI is increasingly widely used, and the accuracy of predictions improves, a likely result is that a smaller proportion of cases will go to trial because the litigants will all have a better understanding of whether they would win or lose. There might, however, be a countervailing increase in the amount of strategic litigation, in which people launch carefully-chosen cases in order to nudge the law in a direction they favour.
The legal singularity
When AI systems are much improved, Alarie asks whether the law could become a solved problem, with machines able to predict with such a high confidence the outcome of any potential lawsuit, so that no cases ever reached a court? He calls this the legal singularity. There could also be a legislators’ singularity, in which AI helped politicians and administrators to frame new laws and adjust existing ones in real time to make them more precise, fairer, and more efficient.
Alarie’s intuition is that the law is not a determinate system – in other words, it will never be possible to forecast all cases accurately. However he does think that the system can and will move a long way towards being determinate from where it stands today, and that everyone will benefit from that happening.
Of course, the beneficent outcome is not guaranteed. The dark version of the legal singularity would have one or more AIs imposing harsh control on all of us, perhaps on behalf of an autocratic ruler, or perhaps in service of its own totalitarian logic. But Alarie is an optimist, and expects that the sunnier version will prevail, especially if enough people of good will start thinking about these issues soon, and working out how to steer the evolution of the law in the right direction.
