For lawyers and clients overwhelmed with the cost and aggravation of conducting electronic discovery, a new, more efficient method is taking hold. This methodology, known as predictive coding or technology-assisted review, was developed by e-discovery companies that claim it can provide a significant shortcut in large document reviews and therefore a substantial cost savings. In the last year, the new methodology has been addressed by courts, including those in New York. Some published research on the effectiveness of the technique has shown promise, and a recent article in The Wall Street Journal reported positive results in a case where a court approved the use of predictive coding over the objection of the party that sought the discovery.
In general terms, predictive coding is a way of using technology to extrapolate to a large set of data the results of human relevance decisions on a subset of that data. The process starts with lawyers who are most familiar with the issues in a case or with a set of document requests reviewing the subset of data. These reviewers generally generate a "seed set" of documents, each document of which is coded for relevance, privilege or other criteria. The seed set will include documents that are deemed both relevant and irrelevant. Those selections are then used by the computer to generate relevance rankings for the larger group of documents. The relevance rankings are then tested by the reviewing lawyers to refine the computer analysis. The process is analogous to a spam filter whereby the lawyers and the computer interact to achieve a level of certainty as to what is relevant. Some published studies maintain that the results of this approach are more accurate than an entirely human review of the results of keyword or Boolean searches. With a computer program doing the sorting work of junior lawyers, the savings in large cases can be substantial.
