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.

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

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

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