The CLOC Annual Legal Operations Institute bills its gathering of corporate legal professionals as the largest in the world, and this year Servient looks forward to participating for its first time. The event, which focuses on optimizing the delivery of legal services to businesses, runs from April 22nd through the 25th.
Servient, Inc. is proud to announce that it will be attending the 2018 Legaltech event in New York City, at Booth 202, from January 30th to February 1st.
Legaltech is the single most prominent forum in the United States for the annual showcasing of software platforms and tech innovations to the legal world. And for our 9th consecutive year, Servient, Inc. will be there to make the case for our eDiscovery and machine learning tools.
The sheer volume of electronic data in the digital age is continually driving changes to the rules of federal practice. So be aware that as of Friday, December 1, 2017, two new amendments to the Federal Rules of Evidence (to Rules 803 and 902) will limit the admissibility of electronically stored information (ESI) under the “ancient documents” exception to the hearsay rule and allow for self-authentication of certain types of ESI.
Lynyrd Skynyrd, the popular 1970s Southern rock band behind the legendary songs “Sweet Home Alabama” (Roll Tide) and “Free Bird”, has been back in the news due to a recent legal ruling that prohibits the distribution of a new film about the influential band.
The case is interesting to eDiscovery professionals because much of the case revolved around missing cellphone text messages of a non-party.
Servient’s machine learning technology has been widely used by legal departments for eDiscovery purposes for many years. But today there is growing realization that the sophisticated machine learning technology can also be applied to other legal and compliance areas of decision-making beyond simple eDiscovery document review. For instance, many compliance-related matters can benefit from harnessing precisely the suite of artificial intelligence capacities that Servient has perfected. The truth is that all human decisions based on examining large amounts of unstructured data can be streamlined in such a way.
The practical application of machine learning to e-discovery, commonly referred to as predictive coding, has begun to move from just a debated topic, to an applied technology. As developers of machine learning explain the efficiencies of their processes, law firms and review companies have the same natural reactions that most do when learning about an industry changing technology. These reactions include the initial confusion of how this complex technology works, the doubt of putting decision making into the hands of computers and the concern of potentially having jobs replaced by the new technology. As discussion of the projected impact of this technology has begun to find its way into the mainstream media, it is the jobs impact issue that seems to be getting a lot of ink these days, perhaps highlighted by the concerns of an economy that is still seeking traction.
Take for example the recently released book - Race Against The Machine: How the Digital Revolution is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy, by Erik Brynjolfsson and Andrew McAfee. In a Yahoo! news blog interview of McAffee, the research scientist for MIT’s Sloan School of Business suggests that a busboy might have less anxiety about job prospects than a lawyer due to advances in technology. This is a comparison that seems a bit far-fetched and one that preys directly on the fears of an uncertain economy.
While I hate to show my age, this blog topic reminds me of a statement I heard at one of the first e-discovery seminars I attended in San Francisco in the 1990s. In those early days, someone from the audience commented that e-discovery will require the parties to cooperate in litigation. A panelist who was then General Counsel of one of the largest tech companies in the world responded with tongue in cheek: “Yes and the litigants will all join arms, sit around a campfire and sing Kumbaya.”
I think it is safe to report that counsel in litigation are still not joining arms with their opponents and singing Kumbaya together. Many lawyers still remind us that e-discovery is part of an adversarial process and clients pay their lawyers to win, not to make friends with the other side.
An essential step to improving quality and efficiency is to measure the effectiveness of the current process. Without measurement, we cannot evaluate the effect that new technology and process changes will deliver.
For years, the e-discovery industry has focused on review speed (number of documents reviewed per hour). There is no doubt that the speed of review is important. But, I would argue that the “responsive rate” is the most important metric when it comes to e-discovery cost containment – and is commonly overlooked.
Anyone who has managed a litigation budget would agree that controlling the cost of litigation often seems like an impossible endeavor. Litigation budgeting has always been challenging because of the unpredictable nature of cases and the inescapable fact that you cannot control the burdens imposed by your adversary or the court (and sometimes your own client).
The volume and nature of data produced by companies today has added a whole new set of challenges to controlling litigation costs. The risks associated with the preservation and collection phase of e-discovery often drives process decisions that increase cost. And, tight deadlines involved in litigation frequently heighten a litigator’s historic resistance to the adoption of new technology solutions and meaningful process improvements.