Artificial intelligence is legal’s new buzzword, but its use and effects on the industry are far from novel.
The article provides a nice overview of the general applications AI has in legal practice and notes that these have been in use in areas like eDiscovery for some time:
“All this stuff that we are talking about today has really been happening for the past couple of years but in more isolated pockets in term of [an] attorney’s leveraging advanced analytics and machine learning,” Dean Gonsowski, vice president of business development at kCura, added.
But while not widespread, the technologies that incorporate AI are not entirely novel to legal practice either. “Many, many companies use machine learning of a different sort in e-discovery—predictive coding [technology assisted review], among others things,” Obenski noted, adding that whether they realize it or not, these practitioners are deploying a form of AI.
Obenski calls machine learning, which is “where the machine is looking at what a person does, deducing some rules from it, and then applying that understanding to the new problems going forward,” one of the two branches of AI.
“Think within contracts—our technology, using machine learning, runs through the text of the contracts and identifies the context that it’s been taught to identify. There’s a training process where the human says these are the important, relevant bits. For example: I want to find an assignment clause or I want to find a termination date or even something much more sophisticated.”
The other branch is decision-making, which, “best represented by expert systems, is more about the humans setting the rules and setting the logic, and the allowing the machine to apply to logic that the humans set to a problem,” he said.
“There are a bunch of different ways in which people are already making decisions then automating that decision-making based on the things that they found,” Obenski explained.
Chapman defines these two branches as two steps in the same process—automation and decision.
“The former (automation) enables the latter (decision-making) because it provides the critical data and context layer. To be AI enabled in-house functions and law firms need a clear data strategy. A data strategy which creates the foundational data layer on which context can be built.”