How can we allow the current culling and manual review process to continue when we know that it cannot begin to fulfill legal obligations to the courts and to clients?
Miami, FL (PRWEB) April 03, 2013
Many judges and lawyers are asking whether they can trust machine learning to replace traditional legal review by contract or staff lawyers in complex litigation.
The first question they should be asking is how can we allow the current culling and manual review process to continue when we know that it cannot begin to fulfill legal obligations to the courts and to clients?
The reality is that the current process consistently and demonstrably fails to identify most of the relevant documents. Holds are often inadequate and the continuing obligation to produce is usually ignored because it is simply too painful to go back after the initial collection. Structured data is rarely given ample consideration due to the specialized knowledge required. Keyword searches used to cull the population have been proven to identify only a fraction of relevant documents, as well as a massive volume of irrelevant content. Contract lawyer review is hardly more accurate than a coin flip. Reviewers are poorly incentivized and often inadequately trained to understand the complexities of the case. The task itself is tedious, making consistency throughout a long day of review difficult with oneself, let alone with the many other reviewers over the course of weeks and months of review.
In the end, the current process is expensive for all parties, disruptive to the enterprises involved, time consuming for the courts as they try to resolve discovery disputes and does little to fulfill the systems's oblibation to the parties involved.
Join Michael McCreary, President and CEO of Rational Retention LLC and Yindalon Aphinyanap, M.D., Ph.D.,
Co-Director Evidence Based Medicine Information Retrieval and Scientometrics Lab as they discuss:
- Not All Machine Learning is Created Equal
- How to Levearge Machine Learning for Complex Litigation Across the Entire eDiscovery Lifecycle