What A.I. means for the Legal Industry

Well, it is at least true to say that “Legal Tech” is a theme that has been dominating the news and thought in the legal industry of late.

After centuries of being some of the slowest adopters of technological innovation, it appears that we have finally reached a “turning point” such that Legal Tech will be a key force defining the next chapter of the legal industry’s evolution.

In this article, we will be focusing on what we consider to be one of the most promising areas of Legal Tech – contract review powered by artificial intelligence (“A.I.C.R.”).

A.I.C.R. is being widely touted as both the savior of and end of the lawyer. We certainly have a view on which outlook is correct!

With all the hyperbole around we wanted to “cut through the fluff” and point to where the real innovation is happening and how that innovation can be most productively harnessed by in-house teams of any size.

So with this article, we seek to give some:

• real insight into what is the current “state of play” of A.I.C.R. technology; and
• practical insight into how that technology can be incorporated into a lawyer’s day-to-day work.

Explaining the jargon

In the same way that the term “drone” seems to now refer to everything in the air that had previously been called a “remote controlled [x]” the term “A.I.” is being used so frequently now, that it is often hard to know what exactly is being talked about.

So for clarity, “A.I.” in the context of the Legal Tech being discussed in this article refers to the combination of 2 distinct technologies:

“Machine Learning” and “Natural Language Processing”

“Machine Learning” refers to a piece of software that can be taught/trained, without the need for explicit programming.

That is, the application is trained to look for patterns in data that it is presented with, and learn from those examples to increase the chances that its future decisions will be “better”.

In the context of Legal Tech, this “learning” typically falls into the subsets of “supervised” and/or “reinforced” machine learning.

Meaning that the datasets presented to the software are “labeled examples” and ideal behaviors are reinforced within a specific context to maximize performance.

As an example, GLS LegalSifter (our A.I.C.R. solution) is presented with examples of “indirect liability” clauses (circa 10,000 examples) with the characteristics of a “successful outcome” being continuously identified and reinforced.

Natural Language Processing (“NLP”) refers to the interaction of computers and human language. With the objective of getting the software to read and ultimately “make sense” of human language in a valuable way.

This is a very hard thing to achieve. Overcoming the ambiguity and imprecise characteristics of human language is a difficult thing to translate into an algorithm.

However, NLP essentially boils down to applying algorithms to a language set in order to identify and extract the natural language “rules” that can be converted into a form that computers can understand.

The Result

By combining Machine Learning and Natural Language Processing together, A.I.C.R systems such as GLS LegalSifter result in software that is trained to look for and identify “legal concepts” and provide in context advice.

This is analogous to having an incredibly advanced version of the beloved “Ctrl + F” function.

The difference being that, for example, the software is not just looking for the words “indirect liability” but rather it is looking for anywhere where the “concept” of indirect liability appears (regardless of the specific words that are used to express it).

Other uses for A.I.

Many efforts are currently also being made to apply A.I. to:

• Due Diligence – i.e. performing due diligence with the help of AI tools to uncover background information.
• Litigation Analytics – i.e. using A.I. to provide statistical probability forecasts of litigation outcomes.
• Intellectual Property – i.e. using A.I. tools to analyse large IP portfolios and drawing insights from the content.
• Electronic Billing – i.e. using A.I. to compute lawyers’ billable hours and combat “time loss” (the bane of so many “traditional” firms).

Debunking some Common Myths

A.I. vs Your Day-to-Day Operations?

In our experience, the businesses that have most effectively and productively incorporated A.I.C.R. into their contracting infrastructure have implemented at least 1 of the following process flows.

We believe that in the not-too-distant future, the vast majority of lawyers will never look at a complex document without the help of A.I.

Again, the point to emphasize here is that a Lawyer + A.I.C.R. is there to:

• ensure that nothing is missed - meaning that work can be delegated to more junior staff (or done by sleepless staff) to enhance margin but with the risk of “human error” being removed ;
• reduce the “review time” needed for a lawyer to physically get through a document. Thereby freeing that lawyer’s capacity to negotiate truly key issues and contribute strategically to driving the deal forward; and
• ensure that your contracting tools (e.g. Contracting Policy, Clause Bank, Playbook, etc.) are efficiently and consistently applied across your contract portfolio – even when you are dealing with third-party paper.

Is A.I. Worth It?

For better or worse (obviously for the better!), lawyers will not be eliminated anytime soon… 

However, A.I.C.R is increasingly becoming an indispensable tool for those lawyers because, realistically, the following benefits can’t be ignored:

• Consistent application of Group Legal and Compliance policies
• Shifts lawyer time from “issue spotting” to "risk assessment" and "strategic advisory"
• Significant reduction of time required to review documents and provide responses
• A.I. “learns” so retention and effective utilization of “deal memory” is enhanced
• Significant reductions in human error
• Tools can be calibrated for both professional and non-professional users
• A.I. adopters better understand new contract support function which 
  increasingly is defined by A.I. tool competencies
• Enhanced junior &non-lawyerr training by providing instant "in context" advice

In summary, by helping lawyers sift through large amounts of data quickly and safely, A.I.C.R. systems such as GLS LegalSifter can enable lawyers to provide better client outcomes faster and therefore cheaper.

To help you assess whether those productivity gains are worth the costs of A.I.C.R. we include the below feedback from GLS LegalSifter for your consideration:

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Please check out the GLS solutions and know-how resources listed on the right side of this page – they might assist your legal team with the issues explored in this Blog. 

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