For more than 10 years, corporate-generated electronically stored information (ESI) has been growing exponentially. The expense of maintaining these growing data stores often stresses corporate bottom lines, but litigation can bring them to the breaking point. Specifically, the greatest concern comes from the document review phase, which in most cases, can take up to 75 percent of the discovery budget. Worse still, many corporate budgets are still unadjusted to account for this reality, and the time frame for production remains the same. Hence, once litigation begins, it often tests the limits of these budgets.
These realities have brought our system of litigating civil disputes to a tipping point, but a solution is in sight. Two respected judges have endorsed a new spectrum of approaches for reviewing documents, known as technology-assisted Review (TAR). In this article, we will be discussing these approaches and when corporations can best apply them.
The workflow leverages AI to look for potentially relevant data, meaning that a computer, rather than a reviewer, is performing the lion’s share of the decision making. The reviewer identifies a handful of potentially relevant documents, and the computer takes this input and looks for “more like this” across the current corpus of data to find what it concludes are also responsive documents.
While this process can be quick and relatively painless to counsel at the outset, as we have seen in recent court opinions, this approach can also present challenges, particularly in the crafting and updating of the seed set. Downstream from the review process, we have also seen that, when challenged, explaining how an analytics-based TAR seed set performs can be difficult.