Ediscovery Feature

machine translation

This is the seventh post in our multi-part series covering our ediscovery chapter of a legal informatics textbook. In this series, we’re covering the history of the Electronic Discovery Reference Model (EDRM), core technical ediscovery concepts, the technologies powering ediscovery (encryption, machine learning, transcoding, etc.); as well as the future of ediscovery. Today we’re covering
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Organization Leads, Now Do More!

Over the years, and the dozens of releases we’ve had here at Everlaw, a consistent theme we’ve tackled is taking many of the more powerful features we’ve built and placing them directly in the hands of our users. For instance, over one year ago we gave case admins the ability to upload your own data
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artificial intelligence ediscovery

Today we’ll continue our weekly series on our ediscovery chapter of a legal informatics book. In this series, we’re covering the ediscovery basics, core technical ediscovery concepts, the technologies powering ediscovery, and the future of ediscovery. We’re pleased today to dive into one of the more popular technologies used in modern ediscovery: machine learning, also often
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Ediscovery text

Communications are one of the greatest unifying forces for civilization. The telegraph revolutionized the ability to share news and conduct business in 19th century; the telephone empowered people to speak across continents in the 20th century. In the 21st century, people can send personal and business communications from handheld devices wherever they have cell phone
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Everyday Discovery Disputes and the Need for Redacting Spreadsheet Cells

Magistrate Judge David Waxse’s Williams v. Sprint/United Mgmt. Co. opinion in 2005 was likely the first to address the challenges of redacting spreadsheets. The discovery disputes in the Williams I opinion included 1) an order to show cause why metadata was scrubbed from spreadsheets; 2) the need for redaction of Social Security Numbers; and 3)
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