‘Responding parties are best situated to evaluate the procedures, methodologies, and technologies appropriate for preserving and producing their own electronically stored information.”1 This Sedona Conference principle increasingly has been cast aside by those urging greater levels of transparency when employing technology-assisted review (alternatively referred to herein as predictive coding or TAR).2 Some producing parties—likely in deference to this growing trend and to avoid a subsequent “gotcha”-style contention that the TAR training exercise was deficient—have even invited opposing counsel into their virtual document review room, where privileged and irrelevant documents abound. But is this “open kimono” concession truly necessary? Or can parties capably discharge their legal and ethical obligations through disclosures of a somewhat less revealing nature? While empirical support is lacking, the surprisingly small number of published decisions and orders addressing predictive coding implies that there may be a silent majority of practitioners employing some variant of TAR alongside other analytical tools without divulging every detail of their culling and review strategy.
Given that predictive coding tools have been promoted largely for their ability to at least displace, if not replace, traditional linear attorney review,3 the level of transparency routinely accepted when conducting such manual reviews provides an instructive point of reference. But first some clarification is in order regarding which facets of the old-fashioned process are actually being supplanted by predictive coding. With respect to preservation and collection, machine-learning technologies have not yet made substantial inroads. In light of the tenuous footing that merely proportional preservation has achieved,4predictive preservation is not likely to gain substantial acceptance anytime soon—the looming risk of spoliation sanctions is just too great. Forensic collection tools likewise are not presently suited to a predictive approach.5
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