It’s 2018 and we’ve finally moved past the point of having to define those pervasive legal tech terms we hear when referring to innovation or anything around technology. Or have we?
I just returned from Legalweek 2018 in New York City and I noticed, not for the first time, that key legal tech terms are bandied about irresponsibly — especially these two: “artificial intelligence” and “analytics.” From the session rooms to the exhibit hall and beyond, much of the misuse of terminology came from the 50 or more e-discovery and case management/document management companies on display, recklessly throwing words around just to make people sit up and listen. I get it: it’s a crowded marketplace and if you aren’t as cutting-edge as the next guy, you aren’t a contender.
Even if your product may be headed toward being AI-backed, or “next generation,” making claims too soon (and inaccurately) is dangerous for a number of reasons. First, if you claim your product is driven by AI, for example, and your technology doesn’t do what you claim it does, that person’s decision-making is now guided by the influence of your sales pitch; they will come to see AI in general as a non-starter. This benefits no one. Throwing jargon around haphazardly actually puts both users and legal tech companies in a difficult position.
Misuse of legal tech terminology means that, for example, when a good AI product comes out, a law firm may say “we’ve already seen AI — it’s no good,” when in fact, the product itself was no good. Let’s not feed the confusion that already exists in legal circles around AI. Let’s develop a common understanding of what AI is, what it can accomplish and where it adds value. Same for “analytics.”
To engender continued growth of the legal tech market, and foster better understanding of these terms, it’s important for the leaders in the industry to be precise with their terminology — it will make it harder for the few “bad actors” to use those words and will inherently hold them to a higher standard. It is not only necessary for the industry to better define these terms, but also to refer to these technologies where they actually apply. To help alleviate the confusion for anyone reading this piece, below are some descriptors that I think explain clearly, in lay terms, what these technologies mean:
Artificial Intelligence: My go-to definition of AI is: “the science of making computers perform tasks that require intelligence when done by humans. AI-powered software can be capable of learning, reasoning, understanding written language and solving complex or ‘fuzzy’ problems.” I have used this definition time and time again, as it leaves less room for interpretation. If you have a better definition, though, I’d love to hear it. (More on this topic in a future post.)
Legal Analytics: This is another legal tech term used with great frequency, and I find this one the most confusing. “Legal analytics involves mining data contained in case documents and docket entries, and then aggregating that data to provide previously unknowable insights into the behavior of the individuals (judges and lawyers), organizations (parties, courts, law firms), and the subjects of lawsuits (such as patents) that populate the litigation ecosystem,” according to Law Technology Today. Broadly, legal analytics can quite literally mean anything.
Let’s all stop clumsily using these words, which makes it harder for users to understand their true value. As a legal ecosystem — of legal services providers, law schools, law firms and legal departments — we need to come to universal agreement on what these important terms mean, use them properly and have a conversation about their value across the sector. Importantly, being as precise as possible with terms will also help alleviate confusion and misunderstandings in the market. Judicial analytics — which is what Gavelytics offers — is narrowly focused on providing actionable data on civil superior court judges. Whereas legal analytics — could be almost anything. See the difference?