Analytics

New technologies are being created and utilized every year. The most significant developments lately are the rise of chatbots - software applications that allow for online chat conversation via text or text-to-speech, without any direct contact with a human operator. Currently, the chatbot garnering the most attention is OpenAI’s ChatGPT program. This article will focus on this technology and how it works either for or against the E-Discovery review process.

Last fall, I had the pleasure of speaking on a panel at the 2019 Relativity Fest entitled “Why Lawyers SHOULD Be Luddites.” It was a lively discussion about whether lawyers should be luddites, whether robots would be taking attorney jobs, should attorneys learn to code, and how to future-proof yourself. Opinions were split but what struck me the most was that a majority - if not all – of the attendees wanted to learn how to future-proof themselves. In this blog I offer tips on how to take advantage of technologies to do just that.

Picture this: Your team has completed the first level and quality control reviews. Your documents have been produced, your privilege log is out the door and you have just received the incoming document production from the opposing party. Now you have a universe of documents with the potential to be used as deposition exhibits, in expert reports, and as trial exhibits. It would be nice if there was a way to take advantage of your previous work identifying hot documents during first level and quality control reviews by using them to identify documents with similar issues or themes in the opposing parties production. You and your team spent days, weeks, or even months reviewing documents for production. Leveraging this time (and costs) would be greatly appreciated by your client. But is that even possible? Yes it is! 

Recently, I 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 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). What follows is my impression of the tool.