Itai Gurari begins his article with a reference to DARPA’s recent announcement of interest in “researching and developing ‘third wave’ AI theory and applications that address the limitations of first and second wave technologies by making it possible for machines to contextually adapt to changing situations.” Gurari welcomes this acknowledgment of the limitations inherent in the machine learning techniques that dominate the field of Artificial Intelligence today – as he defines the subject of this article along with the objective of his company’s work: “While we won’t see significant advances in “third wave” AI for many years to come — or even a jelling around what precisely the “third wave” is — these next generation technologies will likely have a big impact on the field of law, which is a welcome prospect for a field severely in need. Understanding why requires an examination of the first two waves — AI’s past and present — and their critical shortcomings.”
Itai Gurari discusses Judicata’s latest technology solution – Clerk – that evaluates briefs filed in court, grading them on three dimensions: Arguments, Drafting, and Context. The grading reflects factors like how strong the brief’s arguments are, how persuasive the relied upon cases are, and the extent to which the brief cites precedent that supports the desired outcome.
Itai Gurari talks about a new tool from Judicata called Clerk that analyzes and grades briefs, evaluating their strengths and weaknesses, looking for areas of improvement and attack. Clerk’s analysis spans seven dimensions that measure how well the brief is argued, how well it is drafted, and the context within which it arises.
“AI” has become an ever-present marketing buzzword in many sectors, not least of which in the legal arena. Machine learning applications are promising to deliver remarkably accurate software and data solutions while downplaying the critical intersection with the human component. Itai Gurari discusses and illustrates his approach for applying AI to the delivery of accurate legal research by having a human in the loop who is continuously iterating on the technology. In this scenario, the users can rely on a person whenever the problem gets too hard and the technology starts to fail, rather than on an overarching one-size-fits-all machine learning solution.
Legal AI pioneer Itai Gurari’s article is a commentary and a lessons learned that is critical to our communities of best practice as we seek to effectively assess both the promise and significant drawbacks of artificial intelligence and machine learning in the context of the legal sector. As Gurari clearly articulates, building reliably intelligent legal software requires more than just the application of the latest trendy tools. It requires building systems that are robust and that respect the use cases for which they are designed.