Are you still holding off on using artificial intelligence (AI) to expedite your eDiscovery workflows? If so, you’re missing out. Tools that leverage AI technology offer the opportunity to save time and money while improving job satisfaction for eDiscovery professionals.
Just What Is AI, Again?
You might not be entirely sure whether you’re using AI … because you’re not even sure what it is! AI is all around us these days, from smartphone voice assistants to shopping predictors that show us targeted products or advertisements based on our search histories. AI is simply the capability of computers to think like humans. Machine learning, a subset of AI, takes it a step further, enabling computers to analyze data and learn for themselves what’s important.
Some lawyers are reluctant to improve their eDiscovery technology because they aren’t confident in their ability to choose tools wisely or deploy them effectively. Others worry about starting down a slippery slope where, they fear, they’ll be replaced by machines. But for many lawyers, their hesitance to adopt AI solutions relates mostly to a lack of familiarity with how AI works. Fortunately, there’s one AI application in eDiscovery that has gained widespread acceptance: technology-assisted review, or TAR.
TAR Is, Perhaps Fittingly, Black Letter Law
TAR has grown increasingly sophisticated over the years. In that time, it has evolved from a “1.0” approach, which relied on coding from a detailed seed set prepared by a human reviewer, to a “2.0” approach that uses continuous active learning. This advanced form of TAR learns from an ongoing human review process, feeding the human team documents that it believes are relevant and adapting to feedback at it goes.
What was—only a few years ago—a rarely applied tool is now, as the Honorable Andrew J. Peck, Magistrate Judge of the Southern District of New York, has put it, “black letter law.” But TAR isn’t the only application for AI in eDiscovery.
AI Beyond TAR
AI has at least two other common uses in eDiscovery, beyond the review phase.
First, a subset of AI known as natural language processing has enabled computers to start to understand how language works. This technology expedites processing tasks by identifying related terms and phrases and grouping them together. If you’ve used concept clustering, email threading, or near-deduplication tools, you’ve likely taken advantage of these forms of AI. Modern processing tools can crunch through gigabytes of data rapidly, often reducing the volume of information sent out for expensive review by 95 percent or more.
Second, advanced data analytics use AI to help eDiscovery professionals spot patterns and trends in their data. These tools help to identify inefficiencies and opportunities for improvement. For example, analytics might reveal that information from a specific custodian is often relevant to a particular type of matter. That type of institutional knowledge can help to streamline eDiscovery in similar matters going forward.
Not only can AI save you time and money on eDiscovery, but it can also give you a competitive advantage over legal departments and law firms that continue to shy away from technological advancements. Learning about—and getting comfortable with these applications and techniques—is a great way to step ahead of the pack.
Don’t wait any longer to bring your eDiscovery into the twenty-first century. If you need help getting started, let us know—we’d love to show you how you can start making your eDiscovery smarter with AI today.