Includes aspects of the nonlinear review process such as culling, clustering, and de-duplication, but TAR does not meet the requirements for comprehensive predictive coding. According to SME Barry Murphy, there are three main methods for using technology to make legal review faster, less costly, and generally smarter:
Rules-driven. “I know what I am looking for and how to profile it.” In this scenario, a case team creates a set of criteria, or rules, for document review and builds what is essentially a coding manual. The rules are fed into the tool for execution on the document set.
Facet-driven. “I let the system show me the profile groups first.” In this scenario, a tool analyzes documents for potential items of interest or groups potentially similar items together so that reviewers can begin applying decisions.
Propagation-based. “I start making decisions and the system looks for similar-related items.” This type of TAR is about passing along, or propagating, what is known based on a sample set of documents to the rest of the documents in a corpus.