Our Senior Partner, Chris Nott, looks at the developments of AI in litigation funding and its effect on the legal sector.
It seems that the race to predict case outcomes is on; the University of Illinois confirmed they will be able to predict the outcomes of cases, as well as associated costs. Although, they say the technology isn’t there yet, it will be and pretty soon. Also, Peter Wallqvist, the co-founder of AI platform RAVN, announced that they too are looking into developing technology that could predict costs, risks and the outcomes of cases.
This dedication within the LegalTech world to find the sweet-spot algorithm to predict the outcomes of cases poses a huge development for the legal sector. If you’ve perused my LinkedIn Article on AI in the legal sector you’ll know that I don’t foresee the rise of the machines anytime soon. But this is yet another sign that the fat middle aged belly of the legal profession will need to start taking note of the technological developments going on around them.
AI is coming to the legal profession; the answer is to integrate it into how you work and let it help you. In this case the technology could really help lawyers pick cases that have a good chance of standing up in court and reduce the costs for both lawyers and clients.
But let’s not romanticise what it can do; AI sees litigation as a binary process – “start case, win case / lose case” but that’s never how it works in reality. Most cases settle for many reasons, people approach litigation and its risks differently which is why the human factor can never be completely separated from it.
AI can, and will, help us reduce risk and cost but it won’t replace us – not any time soon anyway…
New technology is likely to be one of the most significant factors driving the rise of the litigation funding market over the next five years, industry experts say. The increasing prominence of third-party funding has been accompanied by a rise in the technology and data tools that have the ability to predict, or at least indicate, the outcome of cases. In May this year a machine learning study led by Daniel Katz, a law professor at the Illinois Institute of Technology in Chicago, confirmed that it is possible to use historic data to predict, with a high degree of accuracy, the future decisions of the US Supreme Court.