TAR eDiscovery orders and opinions have made some pretty big splashes in the last five years, and the recent FCA US LLC v. Cummings, Inc., order, despite being brief, was no exception. The court took up the question of whether keyword search culling of a data set prior to the application of Technology Assisted Review (i.e., TAR or Predictive Coding) is the preferred method. The answer, in the court’s opinion, was simple but powerful: it is not.
In Our Experts' Opinions: The Altep Blog
Ever since the March 2, 2015 Rio Tinto opinion and order, there has been a lot of buzz in eDiscovery around the phrase “Continuous Active Learning” (CAL). Judge Peck briefly mentioned CAL while summarizing the available case law around seed-set sharing and transparency. For the sake of clarity, the term seed-set in this post refers to the initial group of training documents used to kick off a Technology Assisted Review (TAR) project. We refer to the review sets that follow as training sets. The point of Judge Peck’s mention of CAL, as I understood it, was to alert readers to the possibility that seed-set selection and disclosure disputes may become much less necessary as TAR tools and protocols continue to evolve.
If you are reading this blog, you have probably heard the story many times by now. Document review is the most expensive part of eDiscovery. Like many, I find myself asking the same question again and again. How can we do it better? One obvious answer is by defensibly reviewing less. The not so obvious part of that answer is the available methods for doing so.
Dynamo Holdings Limited Partnership v. Commissioner
In an order dated July 13, 2016, the U.S. Tax Court once again strongly supported the use of Predictive Coding. The case had already featured some notable opinions and orders on the topic. This recent order is a fun read for analytics nerds and newcomers alike, as the Court did a great job of laying out the associated facts and addressing the typical arguments for and against use of the technology. Here are a few items that caught my attention as I read it.
This article assumes that Technology Assisted Review is being deployed in a production review setting where the user seeks to identify potentially relevant documents from among a larger corpus, and to subject those documents to full manual review. The use of TAR as an investigative or fact finding tool is a more financially flexible proposition, and the efficiency of that approach should be evaluated via separate standards.
There has been some debate in the past few years about the proper role of the Subject Matter Expert (SME) in technology assisted review (TAR) – a discussion which has understandably resulted in plenty of disagreement. There was a time when most blog posts and white papers swore that SME training was the only path to success, but that position looks to have softened some.
By Joshua Tolles and Sara Skeens
Solving Challenges in the Presentation Phase
In our last post, we discussed the value of looking at analytics in e-Discovery with a creative mindset, and a few steps that you can take to expand your problem solving horizons. As we noted there, analytics is most commonly thought of as a tool to be applied during the review phase of the EDRM to control data sizes; however, we'd like to change that. At Altep, we frequently use analytics to solve many more problems than just those found in the production review arena. With a firm grasp on the technology, plenty of curiosity, and a healthy passion for "building a better mouse trap," we have found quite a few areas where analytics can help turn the eDiscovery rat race into a more methodical and scalable process.