Making a Case for Machine Learning to Legal Departments

The report suggests that the increasing volume of digital records makes techniques leveraging machine learning the most cost-effective options to conduct review. Predictive coding or technology-assisted review (TAR), for instance, harnesses supervised machine learning to predict the responsiveness of documents based on prior coding decisions. From our experience, most savvy practitioners see this type of machine learning as an obvious way to increase efficiencies in the review process, but a number of factors have limited adoption. As a result, there is a conspicuous "consumption gap" in legal technology, which emerges from the difference in the current use of technology versus its capabilities. A 2015 PC –TAR Focus Report prepared by the eDJ Group noted that counsel and management often resist analytics technologies due to their limited knowledge of software capabilities, limitations, potential costs, and applications.