Reducing the Risk and Cost of Litigation: An eDiscovery Project from Start to Finish
With the dramatic growth in digital information, corporations and legal teams need new tools to reduce cost and risk. Review for relevance is the single biggest cost in discovery, making up an average 73 percent of the budget according to a recent RAND study.
Technology-assisted review, also known as TAR or predictive coding, incorporates well-known technologies from information retrieval science and brings them to bear on the problem of identifying relevant and non-relevant documents. An authority on what is relevant in a matter trains a machine learning system by reviewing a small number of documents. The system then ranks any number of documents at speeds of approximately 150,000 documents per hour. The quality and consistency of the result exceeds in most cases what a team of experienced reviewers can accomplish if money and time were not a factor. The results are tested and validated through statistical sampling.
Read this white paper for how TAR can reduce cost and risk for corporations and legal teams.