Wednesday, July 12, 2017
Professor Maura R. Grossman has been named a recipient of the 2017 Fastcase 50 award. This annual award conferred since 2011 by legal publishing company Fastcase recognizes 50 innovators, visionaries and leaders in law.
Grossman was selected for this honour because of her contributions to supervised machine learning for legal document review and electronic discovery. Her 2011 paper, “Technology-assisted review in e-discovery can be more effective and more efficient than exhaustive manual review,” published in the Richmond Journal of Law and Technology, established one of the first benchmarks that compared technology-assisted review to human review. It also demonstrated that such processes can yield results superior to those of exhaustive manual review.
“Evaluation of machine-learning protocols for technology-assisted review in electronic discovery,” a paper published in the Proceedings of the 37th International Association for Computing Machinery Special Interest Group on Information Retrieval Conference on Research and Development in Information Retrieval, demonstrated that continuous active learning — a method developed with computer science colleague Professor Gordon V. Cormack — was the most effective method to find substantially all documents related to an information need.
“Maura’s research has made fundamental contributions to the application of machine learning to electronic discovery in legal proceedings,” said Mark Giesbrecht, Director of the David R. Cheriton School of Computer Science. “Her research with Gord Cormack has helped lay the foundation for more cost-effective, efficient and expeditious search processes to meet high-stakes information retrieval requirements.”
In addition to being a research professor in the David R. Cheriton School of Computer Science, Grossman is principal of Maura Grossman Law, a law and consulting firm based in New York City.
Grossman and the other Fastcase 50 award recipients will be celebrated during the 2017 American Association of Law Libraries Annual Meeting, taking place from July 15 to 18, 2017 in Austin, Texas.
