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Legal Translation Trends & Challenges for 2019

For the past decade, the landscape of the legal industry has been changing. Staying up-to-date with modern technology is no longer a focus for high tech firms; it’s a necessity for any law firm preparing to do business in the New Year. To help you get ahead of the curve, our Morningside experts have highlighted the top three trends and challenges we expect to dominate the legal industry in the coming year – and how we are preparing for them.

Top 3 trends and challenges

  • The inconsistent and continually evolving international regulations and attitudes surrounding privacy and data security.
  • The increasing amounts of electronic information generated by interconnected devices on the Internet of Things (IoT).
  • The rise of new technology-assisted review (TAR) tools for processing large sets of foreign-language eDiscovery documents.

Shifting privacy boundaries

There’s a great deal of sensitive information crossing borders these days, including everything from internal communications to contracts to proprietary product information. As a result, nations around the world are actively adjusting their own privacy standards. This is an area that’s likely to remain in flux during 2019 and into the next several years at least.

2018’s EU General Data Protection Regulation (GDPR) agreement, for example, redefined privacy as it applies to data produced by citizens of its participating countries. GDPR details the ways in which that data may and may not be handled, disseminated, and used. It has extraterritorial scope and therefore causes serious ramifications for international law, as evidence admissible in one location may be deemed inadmissible in another.

With regulations such as these under continual revision around the world, keeping up with, and responding to, the latest developments is likely to consume a significant amount of attorneys’ and translators’ time and attention in 2019.

*At Morningside, we’ll be spending more time in 2019 helping our clients ensure that their documents and translations never end up in the wrong hands or in places they shouldn’t go.

eDiscovery and the IoT

As the Internet of Things (IoT) accelerates, evidence can be pulled from a growing number of interconnected devices, and this is having a profound effect on legal strategies and translations. The IoT is everywhere — in cars, in appliances, in smart homes, and in personal electronics, silently collecting and exchanging data with other IoT devices. Among these devices are products such as Amazon’s Alexa, Google Home, and Apple HomePods — all of which share the capability of capturing private conversations since they are always on and waiting to respond to voice command.

While data is (presumably) collected primarily to help IoT devices do their jobs, we expect to increasingly see it being requested and analyzed by law enforcement and by attorneys for what it reveals about the behavior of the owners and, indeed, anyone within “earshot.” This data will in many cases require translation given the international sourcing of IoT devices, and the internal components on which they depend.

This trend has been building for a few years. For example, in a 2017 murder case, the definitive timeline could only be established by leveraging the victim’s FitBit data. In October of 2018, a drive-by shooting suspect remotely wiped an iPhone that had been seized by police. She was charged with two counts of tampering with physical evidence even though her action was actually digital. The case shows how aware criminals have become of emerging vulnerabilities, and how seriously law enforcement takes IoT data-collection as well.

*At Morningside, we expect the data from social media posts and multilingual text chats to become every bit as prominent in litigation as hard drives, USB sticks and old-fashioned paper trails.

TAR on the rise

When eDiscovery captures hundreds or even thousands of electronic documents that need to be processed, it’s a gargantuan job. Technology-assisted review (TAR) makes the process far more manageable. Here’s how it works:

A TAR system needs to be trained to “understand” what it should be looking for during its automated review of large data sets. To train the system to identify information of value, human reviewers submit examples of relevant content with important words, phrases, and names tagged. These tags are used to create coding protocols that teach the TAR system what to look for. Then the TAR system applies the rules it’s learned and identifies and separates subsets of relevant documents for further human review.

*At Morningside, we anticipate an expanding use of TAR as machine learning becomes more capable, and as litigators struggle to process large volumes of data.