In a word, yes. But it’s not what you might think.
The weak link preventing technology-assisted review (TAR) from achieving its true potential is a lack of clarity surrounding the technology—the components, the development, and the distinctions. No doubt, TAR is seeing greater acceptance and refinement in the legal space. But with a deeper understanding of the technology, TAR can be even more useful and effective.
Understanding Technology-Assisted Review
To start, TAR is a process by which reviewers code documents for some target criteria (e.g., responsiveness), and an algorithm uses those coding decisions to efficiently manage the review of the unseen documents—known as “supervised machine learning.” Some TAR processes manage review by categorizing the remaining documents, others manage by ranking the collection. Either way, the goal is to effectively train the algorithm and minimize the number of documents that need to be reviewed to achieve recall objectives for the target criteria.
If coding decisions are not being used to train the algorithm (known as “unsupervised machine learning”), the process simply is not a technology-assisted review process. Therefore, while clustering, near-duplicate analysis, and email threading all use technology to aid in the review process, they are not TAR for purposes of this discussion. [read more]