Category Archives: Continuous Active Learning

Can You Do Good TAR with a Bad Algorithm?

Should proportionality arguments allow producing parties to get away with poor productions— simply because they wasted a lot of effort due to an extremely bad algorithm? That was a question that Dr. Bill Dimm, founder and CEO of Hot Neuron (the maker of Clustify software), posed in a recent blog post, TAR, Proportionality, and Bad Algorithms (1-NN) and it was the subject of our TAR Talk podcast.

This question is critical to e-discovery, and especially relevant to technology-assisted review (TAR). Listen to our short podcast led by Bill, with participants Mary Mack from ACEDS, and Catalyst’s John Tredennick and Tom Gricks, in a discussion on whether one can do “good” TAR with a bad algorithm. Continue reading

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

Moving Beyond Outbound Productions: Using TAR 2.0 for Knowledge Generation and Protection

Lawyers search for documents for many different reasons. TAR 1.0 systems were primarily used to reduce review costs in outbound productions. As most know, modern TAR 2.0 protocols, which are based on continuous active learning (CAL) can support a wide range of review needs. In our last post, for example, we talked about how TAR 2.0 systems can be used effectively to support investigations.

That isn’t the end of the discussion. There are a lot of ways to use a CAL predictive ranking algorithm to move take on other types of document review projects. Here we explore various techniques for implementing a TAR 2.0 review for even more knowledge generation tasks than investigations, including opposing party reviews, depo prep and issue analysis, and privilege QC. 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

Five Questions to Ask Your E-Discovery Vendor About CAL

In the aftermath of studies showing that continuous active learning (CAL) is more effective than the first-generation technology assisted review (TAR 1.0) protocols, it seems like every e-discovery vendor is jumping on the bandwagon. At the least it feels like every e-discovery vendor claims to use CAL or somehow incorporate it into its TAR protocols.

Despite these claims, there remains a wide chasm between the TAR protocols available on the market today. As a TAR consumer, how can you determine whether a vendor that claims to use CAL actually does? Here are five basic questions you can ask your vendor to ensure that your review effectively employs CAL. 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

Optimizing Document Review in Compliance Investigations, Part 2

This article was originally published in Corporate Compliance Insights on August 6, 2018

Using Advanced Analytics and Continuous Active Learning to “Prove a Negative”

This is the second article in a two-part series that focuses on document review techniques for managing compliance in internal and regulatory investigations. Part 1 provided several steps for implementing an effective document review directed at achieving the objectives of a compliance investigation. This installment outlines an approach that can be used to demonstrate that there are no responsive documents to an equivalent statistical certainty – essentially proving a negative.

What Does it Mean to “Prove a Negative?”

The objective of a compliance investigation is most often to quickly locate the critical documents that will establish a cohesive fact pattern and provide the materials needed to conduct effective personnel interviews. In that situation, the documents are merely a means to an end. 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

What Can TAR Do? In This Case, Eliminate Review of 260,000 Documents

Catalyst_Blog_What_Can_TAR_DoMany legal professionals continue to question whether technology assisted review is right for them. Perhaps you are a corporate counsel wondering whether TAR can actually reduce review costs. Or maybe you are a litigator unsure of whether TAR is suitable for your case.

For anyone still uncertain about TAR, Catalyst is offering the TAR Challenge. Give us an actual case of yours in which you’ve completed a manual review, and we will run a simulation showing you how the review would have gone – and what savings you would have achieved – had you used Insight Predict, Catalyst’s award-winning TAR 2.0 platform. Continue reading