Author Archives: Thomas Gricks

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About Thomas Gricks

Managing Director, Professional Services, Catalyst. A prominent e-discovery lawyer and one of the nation's leading authorities on the use of TAR in litigation, Tom advises corporations and law firms on best practices for applying Catalyst's TAR technology, Insight Predict, to reduce the time and cost of discovery. He has more than 25 years’ experience as a trial lawyer and in-house counsel, most recently with the law firm Schnader Harrison Segal & Lewis, where he was a partner and chair of the e-Discovery Practice Group.

Is Mandatory TAR on the Horizon?

Is  mandatory technology-assisted review on the horizon? This was question that Tom Gricks and John Pappas posed in a recent Bloomberg article.

Cost continues to be the primary issue lying at the heart of e-discovery disputes, particularly with the amendments to the Federal Rules of Civil Procedure (FRCP), specifically Rule 26, mandating that the scope of discovery in litigation be proportional to the value and needs of the case. Judges are increasingly being called upon to resolve these disputes and explicitly consider the fact that the cost and efficiency of different review techniques and technologies can still vary widely. Continue reading

TAR 2.0: Using Contextual Diversity to Find Out What You Don’t Know (and a Bit About Zipf’s Law)

In the early technology assisted review (TAR 1.0) era, many thought training with randomly selected documents was important to the success of a TAR review. The fear was that attorneys would bias the TAR algorithm if they selected documents for initial training. When challenged, most relied on the old shibboleth: You don’t know what you don’t know.

Frankly the “don’t know” point made sense at a time when legal professionals were just realizing that their carefully crafted keyword searches were failing because their targets used different terms. If lawyer keywords couldn’t be trusted, why trust lawyer-selected training seeds? And, random selection was the cornerstone of the TAR 1.0 protocol called simple passive learning. The computer passively (randomly) selected all the training documents. Continue reading

What to Expect from Technology-Assisted Review in 2019

This article first appeared in Law360 on January 3, 2019.

2018 will be remembered as a transition year for technology-assisted review. The battle over whether we can use TAR has all but disappeared, and our attention has turned to how we will use TAR — an inquiry with two clear dimensions. In litigation, the question is whether, and to what extent, we will put TAR under a microscope and force the discussion of parameters surrounding the implementation of the technology-assisted review process. Outside of litigation, the focus has shifted to identifying alternative techniques and other applications for TAR technology within the legal space. Continue reading

Where There’s Smoke… Look for Fire! Using Internal Investigations to Protect Your IP

Trade secrets are immensely valuable, and warrant every ounce of protection that can be mustered. And some industries—such as pharmaceutical, biotech, medical device and software development—are particularly at risk for trade secret theft. Years of effort and investment in research and development can simply walk out the front door on a mobile device or thumb drive, or be transferred or disclosed to outside parties through email or personal cloud or social media accounts—in either case, directly winding up a competitor’s hands. Continue reading

Breaking Up the Family: Reviewing on a Document Level Is More Efficient

Lawyers have been reviewing document families as a collective unit since well before the advent of technology-assisted review (TAR).

They typically look at every document in the family to decide whether the family as a whole (or any part of it) is responsive and needs to be produced, or withheld in its entirety as privileged. Most lawyers believe that is the most efficient way to conduct a review. 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

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

Optimizing Document Review In Compliance Investigations, Part 1

This article was originally published in Corporate Compliance Insights on July 17, 2018

Internal/Regulatory Investigations Versus Litigation

Too many corporations approach litigation and compliance investigations the same way, using the same technology, approach and people. But your approach to managing electronic information in internal and regulatory compliance investigations should differ from the one for litigation.

Most of the discussion surrounding compliance investigations focuses on best practices for planning and conducting personnel interviews. This article addresses document review, specifically electronic document review, an equally critical component of the investigation process directed at finding what some refer to as the “truth serum” for controlling those interviews and structuring much of the investigation. 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

The Importance of Contextual Diversity in Technology Assisted Review

How do you know what you don’t know? This is a classic problem when searching a large volume of documents in litigation or an investigation.

In a technology assisted review (TAR), a key concern for some is whether the algorithm has missed important relevant documents, especially those that you may know nothing about at the outset of the review. This is because most modern TAR systems focus exclusively on relevance feedback, which means that the system feeds you the unreviewed documents that are likely to be the most relevant because they are most like what you have already coded as relevant. In other words, what is highly ranked depends on the documents that were tagged previously. Continue reading