52 LEXPERT MAGAZINE
|
SEPTEMBER 2017
| ARTIFICIAL INTELLIGENCE |
ways so influential in dealmaking. "e
art and science of merger execution have
made great strides since the late 1990s —
a period when stock-market frenzy oen
led to a rush to judgment, and ultimately
to buyer's remorse. Since then, a more pru-
dent, systematic approach to mergers and
acquisitions has emerged,"
3
and there is
much discussion of strategic due diligence.
Add to the mix the volumes of corre-
spondence data and other data generated
within corporations. It can be said that AI
is a remedy to the build-up of this data. It's
a computerized response to a computer-
ized problem. "AI is an overarching term
that includes many branches and sub-sets
of technologies. However, the most com-
mon forms of AI in the legal tech sector are
Machine Learning, Deep Learning, and
Natural Language Processing."
4
Deep Learning is used by a computer
"to understand legal language, then com-
pare the language with other contracts to
identify boilerplate vs. custom, measure the
complexity and readability of the language,
and identify the responsibilities, rights, and
terms of an agreement."
5
Natural Language Processing is "needed
for data mining of external 'big data' sourc-
es and for addressing the legacy contract
encoding problem. e latter is the biggest
But humans are still needed to take ma-
chine learning forward. "In a 'supervised
learning' approach, human experts deter-
mine the outputs and the system 'learns'
how to mimic the human experts, as well
as uncover latent variables and interactions
that humans wouldn't have spotted on
their own. 'Unsupervised learning' doesn't
rely on humans for direct training and
stretches into the realm of deep learning."
8
For all of the promise of AI, one of
things inhibiting it is the language bar-
rier between lawyers and computers. is is
changing as avant-garde lawyers decide to
learn coding (while other lawyers dismiss
it, much in the way that, in an earlier ep-
och, typing was discounted as "clerical" by
some male lawyers).
Certain law schools, such as George-
town, offer coding courses. Harvard Law
explains its Programming course: "Law
firms must understand what tasks can be
most efficiently done by custom soware
short-term problem, especially if [the law
firm or department] has grown through ac-
quisition or has not been rigorous in estab-
lishing a standardized clause library. But
there are contract analytics providers (tools
and/or services) that can help depending
on your situation. It's a thorny issue because
of the dense 'legalese' written in various
languages (which have multiple semantic
and legal interpretations) that must be clas-
sified and mined to extract the atomic-level
insights about obligations, rights and risks
of interest. is NLP of 'natural' commer-
cial language can't be scalably addressed by
human coded, rules-based approaches."
6
Machine Learning "refers to computers
that 'learn' from the data they process rath-
er than relying on humans for rules-based
procedural programming to act upon that
data. It not only discovers patterns in data
but also specifically helps correlate various
data inputs and key data outputs, which
helps enable predictive analytics."
7
PHOTO:
SHUTTERSTOCK
DAVID KRUSE
BLAKE, CASSELS & GRAYDON LLP
"Data security is one
area that is a particular
challenge. The start-ups
developing AI tools have not
always focused on security
enough in their early stages
to allow large law firms and
clients to be comfortable."
