The Atlanta Lawyer August/September 2019 | страница 15

Georgia State University College of Law.
“The grandchildren write about what’s new
in their lives, about their schoolwork, their
hobbies, and where they spent their most
recent vacation. While she is interested in
everything her grandchildren do, she needs
only certain pieces of information on which
to base her own actions: whom to send
money, whom to ask for pictures, and so on.”
"Analytical tools could help her to organize
the letters and conceptionally cluster the
information contained in them," says Smelcer.
Similarly, analyzing hundreds (or more)
legal documents “can reveal patterns not
apparent for the human brain – connections
between words, combinations of words and
phrases, and references - that help to extract
rules from the unstructured, inherently
messy legal language,” states Smelcer.
Wait a minute, I hear you cry, how can the
exquisite writings originating in well-educated
lawyers’ minds, their skillfully built argument
structures peppered with wit, wisdom, and
profound considerations, be reduced to a
data cluster or even a mathematical formula?
“The machines do not look at an individual
document,” explains Ben Chapman,
Executive Director of the Legal Analytics
& Innovation Initiative at the Georgia State
University College of Law. “Rather, they detect
connections between many documents,
similarities, and relationships that mean
something. They do not spit out a judicial
opinion,” he clarifies, "they create models
that help predict a certain future outcome."
Why do we need Legal Analytics?
Such quantitative predictions have value,
e.g., for a law firm’s budget and staffing
decisions and for calculating the risks of a
matter. Legal Analytics is mostly Litigation
Analytics. It is used to assess the time frame,
potential outcome, and costs of a lawsuit;
to predict the judges’ behavior based on
precedent; and to evaluate and select credible
expert witnesses based on depositions,
trial transcripts, as well as jury verdicts and
settlements. In addition, it is also used by law
firms to analyze their competitors and adjust
their resources and strategies accordingly.
Legal Document
Data
Lawyer Interprets
Data
Strategic Decisions
Business
Development/
Client Management
What can Legal Analytics do and NOT
do?
Machine Learning classifies decisions as
raising a particular legal issue and helps
with retrieving similar cases. The Lex
Machina program that was developed at
Stanford University originally predicted
outcomes of Intellectual Property claims
based on a corpus of all IP lawsuits in a
ten-year-plus period. It then analyzes
certain features of the cases such as the
identity and behavior of the participants
of the suit. The program also reads and
organizes data to help users gain “insights
and strategic advantage” in federal
antitrust litigation, comments Rachel
Bailey, Legal Data Expert at Lex Machina.
Ravel,
another
Legal
Analytics
program, creates visual maps of
Litigation
cases, so-called network diagrams.
Citation networks include the judicial
history – which cases or arguments did
the judge find most persuasive, what were
the rulings, and what specific language
did she use. Statutory networks detect
relations among entities referred to by or
subject to a particular regulation across
multiples statutes and jurisdictions.
Social networks examine communication
relations among entities, e.g., senders
and receivers of corporate emails, which
is predominantly used in E-Discovery.
Legal QA: Modeled after IBM’s “Watson,”
the Ross program searches large text
collections to locate “snippets,” i.e.,
documents, short phrases, or sentences
that directly answer a user’s question. Like
Watson, Ross learns from user feedback.
(Continues on page 17)
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