Predictive Coding Receives Judicial Stamp of Approval


On Friday, February 24, New York Magistrate Judge Andrew Peck issued an opinion and order in Da Silva Moore v. Publicis Groupe & MSL Group, 11 Civ. 1279 (ALC) (AJP) (S.D.N.Y. Feb. 24, 2012), the first documented case to recognize predictive coding as an acceptable method of searching for electronically stored information ("ESI"). "Predictive coding," or computer-assisted review, is a process that can replace some human review with computer review, thereby reducing the cost of large-scale, e-discovery reviews. A typical technique is to identify a suitable "seed set" of documents that are reviewed by case attorneys and coded for relevancy, privilege, and other factors. Based on that coding, the system is trained to learn what is relevant and what is not; then the system itself predicts or suggests documents that are potentially relevant or similar across the document collection.

The Da Silva Moore opinion may go a long way toward allaying fears of lawyers about the use of this technology because they "no longer have to worry about being the 'first' or 'guinea pig' for judicial acceptance of computer-assisted review." Id. at 25. However, the opinion notes that the use of predictive coding is not suitable for all cases. Clients and their counsel must determine on a case-by-case basis whether predictive coding is appropriate for the case at hand.

The opinion provides insight for clients and counsel when deciding whether or not to adopt the use of predictive coding technology. Judge Peck indicated a greater willingness to accept the parties' ESI search protocol because both sides agreed to its use (at least in concept), and because of defendants' willingness to follow a "transparent" process. Id. at 22. In Da Silva Moore, the defendants agreed to provide plaintiffs with all non-privileged documents used to "seed" the predictive coding system (even the documents judged not relevant to the case), so that plaintiffs could review defendants' relevance coding being used to train the computer.

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