Stop Worrying and Learn to Love Technology-Assisted Review (Part One)

Technology-Assisted Review (TAR) is a concept-based method of document coding that leverages machine-learning techniques with the input of human reviewers to automate the review process.

Extract from article by Russ Beets

Technology-Assisted Review: Or, How I Stopped Worrying and Learned to Love a Computer Program

Recently, I (Russ Beets) began work on a complex litigation case that had millions of documents to review with many moving parts and quick deadlines that made completing assignments daunting, to say the least. We were divided into teams to tackle different aspects of the review (e.g., first level review for production; QC for privilege and privilege logging; preparation for custodian depositions; preparation of evidence to support our case theories; etc.). My team was tasked with locating documents that would help tell our side of the story and provide evidence for our case theories. We determined that simply running targeted searches to find this evidence was not the best approach, in part because the issues were broadly defined and had multiple subparts, and in part because of the sheer number of documents in the database (over 2.5 million records). As an alternative, we decided to utilize technology-assisted review or “TAR” (also known in the industry as predictive coding). Even though I have practiced law for less than 20 years (and don’t consider myself to be “old” per se), I will admit to being a bit “old-school” when it comes to assisted review – likely out of nothing more than a misplaced fear that these types of technologies would make my job obsolete. However, after learning more about the process and using TAR for several weeks in a row, I came to the realization that it is meant to assist attorneys and streamline review, and is certainly not my replacement.

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