Three years after his Da Sliva Moore opinion approving the use of TAR (technology assisted review, computer assisted review, or predictive coding), US Magistrate Judge Andrew Peck says that “the case law has developed to the point that it is now black letter law,” and goes on to list several judicial decisions approving the use of TAR. You can read Judge Peck’s recent opinion in Rio Tinto PLC v. Vale S.A. et al.
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Magistrate Judge Joe Brown’s ruling in Bridgestone marks another win for predictive coding as a way to efficiently review large data sets, but in my opinion, the most interesting part is the Court’s emphasis on “openness and transparency.” The Court has ordered “full” cooperation, including disclosure of “seed” documents―the responsive and non-responsive documents used to teach analytics software how to code the remaining documents. You can read the full Bridgestone order here.
Last week a group of attorneys and paralegals from our litigation department took advantage of an opportunity to hear some advice from e-discovery pioneer U.S. Magistrate Judge Peck on “TAR” (Technology Assisted Review or Predictive Coding) during a live webinar exploring when to employ such technology. Judge Peck himself became ensnared in an e-discovery sideshow for his February 2012 opinion in Da Silva Moore “that computer-assisted review is an acceptable way to search for relevant ESI in appropriate cases.” (That saga has finally come to an end.)
I jotted down a few good takeaways from Judge Peck:
- TAR should not be held to a higher standard than traditional methods. Keyword and linear, eyes on every page review are rife with their own challenges, and there can be no expectation of perfection.
- Cooperation and Transparency. Peck mentioned that all the judges for one state’s entire judiciary recently signed onto The Sedona Conference Cooperation Proclamation, and he suggested that in addition to disclosing relevant “seed” documents (documents that are used to teach a computer’s algorithms how to determine what’s relevant), parties might consider logging and disclosing the non-responsive ones, too (without disclosing privileged documents, of course).
- Peck recommended using analogies to educate a judge who might not be familiar with e-discovery and TAR. He likened predictive coding technology to the spam filters on our emails, something we all rely on and benefit from in our daily lives.
About half of webinar attendees had already used TAR. The webinar was organized by an ediscovery vendor that provides TAR features in the databases it hosts for clients, so that figure is probably skewed. But with the amount of electronic data growing at a rate that’s hard to imagine (and outpacing the amount of storage available to capture it) the percentage of litigators using TAR is bound to increase as well.