top of page

TAR for Smart People Outline - Chapter 13


Here's another installment in my outline of John Tredennick's 'TAR for Smart People'. I last posted an installment on February 10, 2017. This night's installment is on Chapter 13, "Case Study: Using Insight Predict for Review of Rolling Opposing Party Productions Insight Predict Finds 75% of Hot Docs While Cutting Review 92%".

The challenge in the case study involved finding hot documents in a production received from an opposing party in several different installments without having to review them manually. Catalyst touts its Insight Predict system for its ability to use continuous active learning to re-rank every document as new documents are added, and deal with productions that have a small percentage of relevant documents. Attorneys reviewed documents in small batches of 20 which were then fed back into the system to re-rank documents dozens of times each day. The system enhanced the percentage of relevant documents in the attorney batches from 1% to 7%. The majority of key documents in the production were found after only 8& of the production was reviewed.

Catalyst's system also used contextual diversity sampling to find pockets of documents that are unlike those the attorneys have already reviewed.


 
 

Sean O'Shea has more than 20 years of experience in the litigation support field with major law firms in New York and San Francisco.   He is an ACEDS Certified eDiscovery Specialist and a Relativity Certified Administrator.

The views expressed in this blog are those of the owner and do not reflect the views or opinions of the owner’s employer.

If you have a question or comment about this blog, please make a submission using the form to the right. 

Your details were sent successfully!

© 2015 by Sean O'Shea . Proudly created with Wix.com

bottom of page