Proving to a governmental agency that you simply don’t have any responsive documents can be a very costly proposition. Without a plan, your legal team will probably be required to review the entire collection, only to come up empty handed.
The only way to effectively satisfy your obligation short of a full review is to use every conceivable measure to actually find a responsive document, within reasonable statistical bounds. When that effort fails to locate anything of value, you have essentially shown that there are no responsive documents in the collection and any further review would not be worth the effort.
In this live webinar, our speakers will provide practical strategies and tips for “proving a negative,” without breaking the bank.
They will discuss how to ...
- Set reasonable statistical bounds to circumscribe the review
- Leverage every search and analytics method at your disposal
- Use continuous active learning to extract any responsive documents
- Maximize the breadth of your search using contextual diversity
Our speakers will leave plenty of time for questions. All those registered will receive a link to the recorded webinar.
Thomas C. Gricks III
Managing Director of Professional Services
Tom is a managing director at Catalyst and a licensed attorney in Pennsylvania. Before joining Catalyst, he was a general commercial litigator for 23 years. He practiced before both federal and state courts across the United States, and, in addition to Pennsylvania, is admitted to practice in a number of federal jurisdictions, including the Supreme Court. For the past several years, Tom has devoted a substantial portion of his practice to e-discovery, with a particular emphasis on technology assisted review (TAR). He argued the Global Aerospace case before the Circuit Court in Loudoun County, Virginia, which is the first case to permit the use of TAR over the objection of the opposing party.
Jeremy Pickens, Ph.D.
Jeremy is one of the world’s leading search scientists and a pioneer in the field of collaborative exploratory search, a form of search in which a group of people who share a common information need actively collaborate to achieve it. Jeremy has six patents pending in the field of search and information retrieval, including two for collaborative exploratory search systems. At Catalyst, he researches and develops methods of using collaborative search to achieve more intelligent and precise results in e-discovery search and review. He also studies other ways to enhance search and review within the Catalyst system.
Director of Machine Learning & Analytics
Andrew is the director of machine learning and analytics at Catalyst, and a search and information retrieval expert. Throughout his career, Andrew has developed search practices for e-discovery, and has worked closely with clients to implement effective workflows from data delivery through statistical validation. Before joining Catalyst, Andrew was a data scientist at Recommind. He has also worked as an independent data consultant, advising legal professionals on workflow and search needs. Andrew has a bachelor’s degree in linguistics from the University of California, Berkeley and a master’s in linguistics from the University of California, Los Angeles.