Shining a Light into the Black Box of E-discovery Predictive Coding

http://ow.ly/bdhrl

An article by Matthew Nelson posted on law.com on the Corporate Counsel webpage.

This article discusses issues that relate to predictive coding, and provides some insight into the definition of the term, as well as the state of acceptance of the technology by the judiciary.

The article states, "In 2012, the wait for judicial guidance ended abruptly when not one, but three new predictive coding cases surfaced: Da Silva Moore v. Publicis Groupe; Kleen Products, LLC v. Packaging Corporation of America; and Global Aerospace Inc., v. Landow Aviation, LLP. In Da Silva Moore, Judge Andrew Peck even approved the use of predictive coding technology in “appropriate cases,” leaving some to believe the courthouse doors had been thrown open to unbridled use of the technology. Somehow, within weeks of the decision, the wheels of the predictive coding freight train locked up, leaving many wondering whether or not these new predictive coding cases provided clarity or merely added more confusion."

The author further states, "Predictive coding is a type of machine-learning technology that enables a computer to automatically predict how documents should be classified based on limited human input. The technology is exciting for corporate legal departments attempting to manage skyrocketing litigation budgets, because the ability to automatically rank and then “code” or “tag” electronic documents, based on criteria such as “relevance” and “privilege” has the potential to save companies millions in e-discovery costs. The savings are directly attributable to the fact that fewer dollars are spent paying lawyers to review every document before documents are produced to outside parties during discovery.

The main advantage for corporations is that a fraction of documents are reviewed which results in a fraction of the review costs. The process begins by feeding “relevance” and “privilege” decisions made by attorneys about a small number of case documents called a “seed set” into a computer system. The computer then relies on these “training” decisions to create an algorithm that ranks and codes the remaining documents automatically. The attorneys can then evaluate the accuracy of the computer’s automated decisions."

The article examines 3 recent cases that addressed the possible use of predictive coding.   The article looks at the Da Silva Moore case; the Kleen Products case; and the Global Aerospace case, all of which requested some form of court approval for the use of predictive coding.

The author goes on to write, "The turmoil surrounding the first batch of predictive coding cases has led some to question whether or not predictive coding technology is ready for mainstream adoption. Those on the conservative side will wait and see how the above cases unfold before testing the predictive coding waters. More progressive legal departments will capitalize on the lessons these cases illustrate, instead of being duped into thinking the future of predictive coding technology has been significantly tarnished or delayed.
 
Savvy practitioners will recognize these cases reveal that first-generation technologies lack the transparency and simplicity necessary to take predictive coding mainstream in the legal profession. They will also recognize that technological change and improvement designed to automate a complex new approach to document review is around the corner. Academic studies indicate that predictive coding technology truly can yield far superior results to manual review—at less cost—when managed properly. (See Maura R. Grossman and Gordon V. Cormack, "Technology-Assisted Review in E-Discovery Can Be More Effective and More Efficient Than Exhaustive Manual Review" [PDF], XVII, RICH. J.L. & TECH, 11 [2011].)  A link to the referenced article is provided.