Developments in legal technology are allowing for a deeper analysis of court decisions, including how specific judges tend to rule, whether certain motions are accepted or denied, and the specific information contained in dockets or rendered decisions which can then be utilized in case strategy. Such information has always been available, but has never before been compiled and analyzed into a single data set. However legal research and analytics firms such as Ravel Law, Lex Machina and others are spearheading developments in predictive technology. In Vannin Capital's latest edition of
Funding in Focus, Managing Director Yasmin Mohammad sat down with Ravel Law's Co-Founder Daniel Lewis to discuss the impact AI is already having on the legal industry, and the potential for even greater impact down the road.
Ravel Law allows users to search caselaw quickly and easily, and discover analytical insights regarding judges, courts, cases, and firms. "For example, a litigator can see what percentage of the time a judge grants a motion to dismiss in a particular type of case (e.g. product liability), and discover the language and cases that the judge commonly uses and is influenced by in such decisions," says Lewis. Attorneys can use Ravel Law to make data-driven decisions about case strategy and potential outcomes. In fact, Lewis' firm is already working on the next logical step in that equation - how to connect analyzing the past with predicting the future. Part of the challenge is deciphering which variable - judge, lawyer, motion type, case type, etc. - is responsible for the given outcome. "Saying two variables are highly correlated does not mean one is causing the other; both could be caused by a third, unidentified variable, or it could be a random correlation, or the dataset could be biased or simply too small. Dispute resolution analytical technology currently consists of identifying correlations. It takes an experienced lawyer to review the data and understand the valuable, actionable insights and random patterns that are irrelevant." So even though companies like Ravel Law are utilizing machine learning to enhance attorney-client outcomes, the days of attorneys being supplanted by fully autonomous AI machines are still a ways away. As far as international arbitration is concerned, there are a pair of hurdles which stand in the way of the widespread usage of machine learning: (1) awards are not public information for the most part in commercial arbitration and only partially in investment treaty arbitration; and (2) while tribunals do look to certain decisions for guidance, they only do so in an informative manner (with the exception of a dozen truly authoritative decisions most often quoted). A lack of recorded precedent decisions means there is a small dataset, which limits the ability of AI to effectively predict future outcomes. However that hasn't stopped some firms from utilizing machines in the field of international arbitration. "In the context of international arbitration, I am aware of various firms that have used AI technology in performing voluminous document reviews," said Sammaa A.F. Haridi, Partner at Hogan Lovells US LLP. "There have been a number of studies on this and the results show that the use of AI can produce reliable results for clients at a lower cost." As
LFJ recently reported, Daniel Katz, a law professor at Chicago’s Illinois Institute of Technology, confirmed that it is possible to use historic data to predict, with a high degree of accuracy, the decisions of the US Supreme Court. AI-driven legal research firms like Ravel Law are taking full advantage, and their products could influence the legal landscape for years to come.
