This is the first of a three-part series concerning technology assisted review of documents in civil litigation. In the first part, we assess various methods of such review and discuss when it should be considered for use in civil litigation. The second and third parts will focus on how to make technology assisted review work for your company in litigation while achieving cost savings.

Your company is engaged in a large civil action, and discovery has begun. The discovery requests you have received from opposing counsel seek voluminous electronic data, and the idea of using key word searches and manual attorney review strikes you as both unwieldy and costly. As recently as five years ago, you would not have had any attractive alternatives. Now, however, technology assisted review (TAR) — also referred to as predictive coding or computer-assisted review — has evolved into a viable method to gather, review and produce relevant documents in litigation. The pertinent questions become whether you should use TAR, and how best to fit its use within your company’s broader litigation strategy.

Given its relative newness, not all attorneys understand what TAR is or how it works. In a nutshell, TAR involves taking a subset of documents reviewed by attorneys and marked for responsiveness and then, using specialized software, applying the review decisions made for that subset to all remaining un-reviewed documents. In supervised learning TAR, a type of predictive coding, the software simply looks for documents that have the same characteristics as the relevant documents in the subset. In active learning TAR, also a predictive coding method, you start with an initial “seed set” chosen by attorneys and fed into the program, which returns samples of potentially responsive documents and asks an attorney to decide whether they are, in fact, responsive. This method is more iterative and interactive. Finally, in knowledge engineering TAR, software replicates how an attorney thinks about complex problems, creating a decision tree (artificial intelligence) that the computer uses to determine whether a document is responsive. Recent research has shown that TAR produces results that are at least comparable to manual review, with active learning and knowledge engineering TAR demonstrating 10 percent greater accuracy.

Companies and their counsel need to consider the nature of the case when deciding whether to use TAR. For example, small matters with low financial stakes, few informational custodians, or limited amounts of electronic data are not good candidates for TAR. Cases in which responsive electronic data contains images, abundant numerical data, short text messages or complex coding schemes are also not ideally suited for TAR due to the nature of that information. On the other end of the spectrum, those dealing with cases involving high financial stakes, large volumes of mostly textual electronic data (such as e-mail), and short discovery periods will find TAR particularly helpful. In these cases, proportionality concerns should be foremost, as courts are becoming more willing to reduce the scope of discovery requests if searching for and producing responsive documents is more costly than the value of the case itself.

If you believe TAR would help make discovery in a particular case more efficient and manageable, you should ensure that your company’s attorneys are knowledgeable about the technology and engaged throughout the e-discovery process. Thoughtful advice at the subset or seed set stage of review, and continued involvement in the application of TAR, will improve both the accuracy of the results and the likelihood that the court will approve them. Working closely and effectively with opposing counsel will also help ensure that TAR withstands judicial scrutiny.

As the technology advances and more lawyers become familiar with the advantages TAR offers, an ever greater number of parties are likely to use its methods in electronic discovery. The final two parts of this series will provide practical advice on managing the use of TAR vis-à-vis opposing counsel and achieving cost efficiency.