TAR first rose to prominence in the legal industry around 2011 under the name predictive coding. Predictive coding largely was abandoned in favor of the more generic term TAR – or, sometimes, computer-assisted review (CAR). TAR and CAR have also joined by TAR 2.0 and by CAL (continuous active learning), while LSI and PLSA are joined by SVM and other new acronyms – creating an alphabet soup.
Our monthly legal eDiscovery news round-up for August 2020 features new privacy updates, new state regulatory updates, and continuing adaption to COVID-19, as well as new noteworthy cases, new useful publications, and a variety of new XDD educational content.
We’ve now seen in Leidig v. BuzzFeed and NDLON v. ICE that the risks and consequences of employing self-collection approaches are not merely hypothetical. One self-collection failure resulted in evidence preclusion, and the other resulted in substantial additional discovery. Let’s conclude our discussion of self-collection risks with a look at three more case examples.
The risks and consequences of employing self-collection approaches are not merely hypothetical. For many years, courts have highlighted those risks, have taken parties and their lawyers to task for their reliance on self-collection in the face of those risks, and have applied significant monetary and evidentiary sanctions for failures caused by taking those risks.
We’ve now seen the myriad ways that allowing custodians to collect their own materials can increase the risks of downstream issues and negative outcomes. But, what about having internal information technology personnel collect instead? While marginally lower risk than allowing custodians to collect their own materials, having IT carry out organization self-collection is still risky and, potentially, disruptive.
Improper or incomplete collection can undermine preservation, review, production, and authentication – potentially creating a whole host of downstream issues and potential grounds for sanctions. So, how does self-collection increase the risks of these issues and outcomes? Let’s start by reviewing the risks of allowing custodians to collect their own materials.
Collection activities are integral to creating the foundation for all the discovery work that follows. Particularly at a time when legal departments and law firms alike are looking to tighten their belts, collection can seem a tempting place to take shortcuts in the name of savings. Not all shortcuts are created equal, however, and self-collection is one that should almost always be avoided.
Our monthly legal eDiscovery news round-up for July 2020 features the end of the EU-U.S. Privacy Shield Program, a new safe harbor in New Jersey, law firm cybersecurity concerns, and continuing adaption to remote work due to COVID-19, as well as new noteworthy cases, new useful publications, and a variety of new XDD educational content.
The EU-U.S. Privacy Shield is a framework that – until recently – was relied upon by more than 5,000 organizations to transfer data from the EU to the U.S. without running afoul of EU privacy protections. On July 16, 2020, the Court of Justice of the European Union (“CJEU”) issued a judgement invalidating the Privacy Shield framework and casting doubt on whether Standard Contractual Clauses (“SCCs”) can be used for EU-to-U.S. transfers instead.
By October 2019, Microsoft reported that it had surpassed 200 million commercial monthly active users of Office 365 (now called Microsoft 365). With such widespread usage, Microsoft 365 has become a common discovery source and a challenging one. So, just what sorts of materials might be in Microsoft 365? What eDiscovery features might be available? What limitations and complications do practitioners need to consider?
Our monthly legal eDiscovery news round-up for June 2020 features continuing industry adaptation to COVID-19 and some new data privacy developments, as well as new noteworthy cases, new useful publications, and a variety of new XDD educational content.
For several years now, use of the workplace collaboration and messaging tool Slack has been growing exponentially, first augmenting and then starting to supplant email usage within many organizations. With such widespread usage, Slack has started to become a common source for discovery. So, just what sorts of materials are in Slack? How can those materials be preserved and collected? What special challenges does Slack data present?