Category Archives: Insight Predict

Was It a Document Dump or a Deficient TAR Process?

TAR TalkThat’s the topic of our recent TAR Talk podcast.* We talked about the recent decision by the U.S. District Court for the District of Columbia In Re Domestic Airline Travel Antitrust Litigation, 2018 WL 4441507 (D.D.C. Sept. 13, 2018), an antitrust class action lawsuit against the four largest commercial airlines in the United States—American Airlines, Delta Air Lines, Southwest Airlines, and United Airlines.

The declarations around this decision prompted much discussion in the e-discovery world, particularly for those using technology-assisted review (TAR) in the review process. The argument was based on United’s core document production. The plaintiffs called it a deficient TAR process and complained that they were forced to review mountains of non-relevant documents (aka, a document dump). Continue reading

How Can I Use TAR 2.0 for Investigations?

Across the legal landscape, lawyers search for documents for many different reasons. TAR 1.0 systems were primarily used to classify large numbers of documents when lawyers were reviewing documents for production. But how can you use TAR for even more document review tasks?

Modern TAR technologies (TAR 2.0 based on the continuous active learningor CALprotocol) include the ability to deal with low richness, rolling and small collections, and flexible inputs in addition to vast improvements in speed. These improvements also allow TAR to be used effectively in many more document review workflows than traditional TAR 1.0 systems. Continue reading

Predict Proves Effective Even With High Richness Collection

Finds 94% of the Relevant Documents Despite Review Criteria Changes

Our client, a major oil and gas company, was hit with a federal investigation into alleged price fixing. The claim was that several of the drilling companies had conspired through various pricing signals to keep interest owner fees from rising with the market.1 The regulators believed they would find the evidence in the documents.

The request to produce was broad, even for this three-letter agency. Our client would have to review over 2 million documents. And the deadline to respond was short, just four months to get the job done. Continue reading

57 Ways to Leave Your (Linear) Lover

A Case Study on Using Catalyst’s Insight Predict to Find Relevant Documents Without SME Training

A Big Four accounting firm with offices in Tokyo recently asked Catalyst to demonstrate the effectiveness of Insight Predict, technology assisted review (TAR) based on continuous active learning (CAL), on a Japanese language investigation. They gave us a test population of about 5,000 documents which had already been tagged for relevance. In fact, they only found 55 relevant documents during their linear review.

We offered to run a free simulation designed to show how quickly Predict would have found those same relevant documents. The simulation would be blind (Predict would not know how the documents were tagged until it presented its ranked list). That way we could simulate an actual Predict review using CAL. Continue reading

Catalyst Insight Review By Brett Burney

Recently, Brett Burney, e-discovery consultant and founder of Burney Consultants, reviewed Catalyst’s Insight Discovery search and review capabilities, which are part of Catalyst’s full EDRM platform comprising of Insight Legal Hold and Collect, Search & Review, Predict and Business Intelligence. We invited Brett to share his insights as a guest author.

Catalyst Insight – Lightning Quick, Responsive Review Platform for Instantly Searching Millions of Digital Files with a Built-In Continuous Active Learning Predictive Analytics Engine

It seems that Catalyst has always been on a mission to push the boundaries of applying advanced text analytics to enormous amounts of electronically stored information for eDiscovery and investigatory purposes… and it’s exciting to watch. Continue reading

How to Get More Miles Per Gallon Out of Your Next Document Review

How many miles per gallon can I get using Insight Predict, Catalyst’s technology assisted review platform, which is based on continuous active learning (CAL)? And how does that fuel efficiency rating compare to what I might get driving a keyword search model?

While our clients don’t always use these automotive terms, this is a key question we are often asked. How does CAL review efficiency1 compare to the review efficiency I have gotten using keyword search? Put another way, how many non-relevant documents will I have to look at to complete my review using CAL versus the number of false hits that will likely come back from keyword searches? Continue reading

TAR for Smart Chickens

Special Master Grossman offers a new validation protocol in the Broiler Chicken Antitrust Cases

Validation is one of the more challenging parts of technology assisted review. We have written about it— and the attendant difficulty of proving recall—several times:

The fundamental question is whether a party using TAR has found a sufficient number of responsive1 documents to meet its discovery obligations. For reasons discussed in our earlier articles, proving that you have attained a sufficient level of recall to justify stopping the review can be a difficult problem, particularly when richness is low. Continue reading

Review Efficiency Using Insight Predict

An Initial Case Study

Much of the discussion around Technology Assisted Review (TAR) focuses on “recall,” which is the percentage of the relevant documents found in the review process. Recall is important because lawyers have a duty to take reasonable (and proportionate) steps to produce responsive documents. Indeed, Rule 26(g) of the Federal Rules effectively requires that an attorney certify, after reasonable inquiry, that discovery responses and any associated production are reasonable and proportionate under the totality of the circumstances.

In that regard, achieving a recall rate of less than 50% does not seem reasonable, nor is it often likely to be proportionate. Current TAR decisions suggest that reaching 75% recall is likely reasonable, especially given the potential cost to find additional relevant documents. Higher recall rates, 80% or higher, would seem reasonable in almost every case. Continue reading

Comparing Family-Level Review Against Individual-Document Review: A Simulation Experiment

Catalyst_Simulation_ExperimentIn two recent posts, we’ve reported on simulations of technology assisted review conducted as part of our TAR Challenge—an opportunity for any corporation or law firm to compare its results in an actual, manual review against the results it would have achieved using Catalyst’s advanced TAR 2.0 technology, Insight Predict.

Today, we are taking a slightly different tack. We are again conducting a simulation using actual documents that were previously reviewed in an active litigation. However, this time, we are Continue reading

The TAR Challenge: How One Client Could Have Cut Review By More Than 57%

Catalyst_TAR_Challenge_Client_Save_57_PercentHow much can you save using TAR 2.0, the advanced form of technology assisted review used by Catalyst’s Insight Predict? That is a question many of our clients ask, until they try it and see for themselves.

Perhaps you’ve wondered about this. You’ve read articles or web sites talking about TAR’s ability to lower review costs by reducing the numbers of documents requiring review. You might even have read about the even-greater gains in efficiency delivered by second-generation TAR 2.0 platforms that use the continuous active learning protocol. But still you’ve held out, maybe uncertain of the technology or wondering whether it is right for your cases. Continue reading