ARCHIVED CONTENT
You are viewing ARCHIVED CONTENT released online between 1 April 2010 and 24 August 2018 or content that has been selectively archived and is no longer active. Content in this archive is NOT UPDATED, and links may not function.By Jeremy Pickens
In a recent blog post, Ralph Losey lays out a case for the abolishment of control sets in e-discovery, particularly if one is following a continuous learning protocol. Here at Catalyst, we could not agree more with this position. From the very first moment we rolled out our TAR 2.0, continuous learning engine we have not only recommended against the use of control sets, but we actively decided against ever implementing them in the first place and thus never even had the potential of steering clients awry.
Losey points out three main flaws with control sets. These may be summarized as (1) knowledge Issues, (2) sequential testing bias, and (3) representativeness. In this blog post I offer my own take and evidence in favor of these three points, and offer a fourth difficulty with control sets: rolling collection.