"[C]omputer-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."
— U.S. Magistrate Judge Andrew J. Peck, Southern District of New York, Da Silva Moore v. Publicis Groupe & MSL Group (2012)
Is computer-assisted review, or predictive coding, the holy grail for in-house counsel? Will it finally curb the explosive growth in costs for electronic discovery in large-data-volume cases?
Not yet, but predictive coding is here to stay. If used effectively, predictive coding can be a valuable tool, significantly cutting fees generated by outside counsel to review and code documents in large, complex litigation. Using predictive coding effectively, however, typically requires buy-in from all parties — as well as the court, if one side objects to its use.
As most in-house counsel know, predictive coding relies on computer software and proprietary algorithms to determine whether electronic documents are relevant to a case. Predictive-coding software is similar to spam-filters that train a computer program to identify unwanted, mass emails.
Litigation team members feed the software a seed set of potentially relevant documents to begin training the software. After lawyers identify the seed set, senior attorneys provide additional feedback to the software, which teaches it what is truly relevant. This process may be repeated several times.
The software then analyzes the seed set and identifies common characteristics, such as people, places, words or concepts. These allow the software to create rules it can apply across all harvested documents.
Once the software is sufficiently trained and it has established these rules, the litigation team can use the software to cull responsive documents from the entire universe of data, potentially saving the client significant attorney-hours in review time.
While some have raised concerns about the accuracy of computer-assisted review, Peck wrote in Da Silva that "statistics clearly show that computerized searches are at least as accurate, if not more so, than manual [document] review." Despite this, it still may take time for lawyers and courts to accept predictive coding.
Predictive coding, like traditional methods of handling electronically stored information in litigation, works best when the parties agree on the type of electronic files to search, the custodians whose files they will search and the protocol for searching those files.
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