Presented by Catalyst and ACEDS

At one time or another, most e-discovery professionals have felt the unwelcome pressure of a slow-moving document review. Pressure to speed review and keep costs in line is a major reason that e-discovery is ground zero for today’s exploding use of artificial intelligence (AI) in law.

Get Instant Access to This Webinar:

Machine learning is an area of AI that enables computers to self-learn, without explicit programming. In e-discovery, machine-learning technologies such as technology-assisted review (TAR) are helping legal teams dramatically speed document review and thereby reduce its cost. TAR learns which documents are most likely relevant and feeds those first to reviewers, typically eliminating the need to review from 50 to 90 percent of a collection.

Lawyers are getting it, as evidenced by their expanding use of TAR. At Catalyst, 50 percent of matters now routinely use TAR—and none have been challenged in court. A recent Bloomberg BNA survey found that 87 percent of those who tried TAR found it effective.

This webinar will introduce you to the latest TAR technologies, review the legal and ethical issues around its use, and discuss how and when to best use it.

Join us to learn about: 

  1. Technical and legal bases for the use of TAR in litigation
  2. Implications of Federal Rule of Civil Procedure 26(g)
  3. How a new algorithm improved efficiency by 20 - 40%
  4. Examples and lessons learned from more than 200 cases
All those registered will receive a link to the recording, the Bloomberg survey, and the electronic version of the new book, Ask Catalyst: A User’s Guide to TAR.

 

Speakers:

jeremy pickens bg
Jeremy Pickens, Ph.D. | Chief Scientist, Catalyst

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.

Aaron Crews
Thomas C. Gricks III | Managing Director, Professional Services | Catalyst

Tom is a managing director at Catalyst and a licensed attorney in Pennsylvania. Before recently 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 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.

Andrew Bye
Andrew Bye | Director, Machine Learning & Analytics | Catalyst

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.

Contact

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