Traditional document review in the age of e-discovery is reaching the point of infeasibility. Setting hordes of attorneys in front of computer screens to review and code millions (sometimes billions) of records is not only prohibitively expensive, but often results in errors and inconsistent quality. At Morgan Lewis, the eData team is leveraging the combination of predictive coding and Lean Six Sigma techniques to offer clients higher-quality, lower-cost document review and thus a promising solution to the volume problem.
PREDICTIVE CODING
In order to reduce discovery costs, our focus is on defensible ways to reduce the volume of documents that require human review while maintaining (or even improving) the accuracy rates of those reviews. In most litigation, a lot of electronically stored information is collected and pushed through the e-discovery pipeline until it lands at the most expensive part of the process: attorney review. Predictive coding is an innovative tool that can help reduce the cost and also increase the accuracy of human document review by leveraging technology to reduce data volumes that require attorney review while enhancing the speed and quality of the review.
The current industry standard is to use keywords, deduplication and similar objective culling criteria to reduce the volume of data and then to perform a linear human review of any records that remain. Predictive coding can eliminate, escalate, categorize, and prioritize records for review, thus decreasing data volumes and enhancing human review.
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Source: law.com
By: Stephanie A. Blair and Tara Lawler
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