The difficulty with implementing processes such as predictive
coding is that the technology is so new that these methods are
fairly untested in court. However, the first wave of cases
discussing the propriety of predictive coding has illustrated that
it is indeed going to be accepted as an appropriate discovery tool.
Earlier this year, in a landmark decision, U.S. Magistrate Judge
Andrew J. Peck for the Southern District of New York authorized the
use of predictive coding in Da Silva Moore v. Publicis
Groupe, No. 11-CV-1279, 2012 U.S. Dist. LEXIS 23350 (S.D.N.Y.
Feb. 24, 2012). Peck summarized his position, stating: "What
the bar should take away from this opinion is that
computer-assisted review is an available tool and should be
seriously considered for use in large-data-volume cases where it
may save the producing party (or both parties) significant amounts
of legal fees in document review." The district court
ultimately adopted Peck's evidentiary rulings in Da Silva
Moore v. Publicis Groupe, No. 11-CV-1279, 2012 U.S. Dist.
LEXIS 58742 (S.D.N.Y. Apr. 26, 2012). In his opinion, Peck was
careful to point out that the plaintiffs consented to the
defendant's use of predictive coding and the discovery dispute
merely concerned the implementation of its use. Thus, lawyers were
left to wonder what would happen if the parties did not agree to
the use of predictive coding.
The same week that Peck's
ruling was affirmed, a state court judge in Virginia approved the
use of predictive coding in a case over the objections of the
opposing party. In Global Aerospace v. Landow Aviation,
No. CL 61040 (Vir. Cir. Ct. Apr. 23, 2012), 20th Judicial Circuit
Judge James Chamblin ordered that defendants could use predictive
coding, despite the plaintiff's objections that the technology
was not as effective as manual review. Chamblin disagreed and
ordered the predictive coding for the production of the
defendant's ESI, provided that the receiving party would still
have an opportunity to question the completeness of the contents of
the production or the ongoing use of predictive coding. This
opinion, although limited in its direct impact in other litigation,
along with Peck's decision in Da Silva Moore,
indicates willingness by the judiciary to incorporate predictive
coding into e-discovery.
Predictive coding and other
automated methods of e-discovery obviously have limitations. Peck,
in Da Silva Moore, emphasized that his approval of
predictive coding was not universal: he did not order the use of
predictive coding, he stated that computer-assisted review is not
required in all cases, and that he did not endorse any particular
vendor or predictive coding tool. In addition, it is the
responsibility of the lawyers to understand the predictive coding
program and how it works so that they can demonstrate the
method's reasonableness if it is called into question. Just as
with the traditional e-discovery tool of keyword searching, lawyers
must engage in careful planning and sufficient quality control to
ensure the accuracy of the program. Finally, lawyers should
cooperate with opposing counsel and be transparent in their use and
the scope of predictive coding in order to avoid unnecessary
discovery disputes.
Published in The Legal
Intelligencer on June 27, 2012
The content of this article is intended to provide a general
guide to the subject matter. Specialist advice should be sought
about your specific circumstances.
Specific Questions relating to this article should be addressed directly to the author.
A discussion on some practical tools lawyers can use, or consider using, in order to decrease defense costs and to increase the chance of concluding a case sooner rather than later.
Carlton Fields recently published a survey (pdf) of 368 general counsel and other in-house counsel at major companies across more than 25 industries regarding the class actions they faced in 2012 and their expectations for 2013.
A financial services organization is a party to a litigation in which plaintiffs are seeking information concerning business practices that impacts a number of the organization's customers.
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