Category Archives: Technology-Assisted Review

Yee Haw! Texas Lawyers Required to be Competent in Technology Under Revised Rule 1.01

A big yee-haw goes out to the Texas Bar as they recently became the thirty-sixth state in the Union to codify what the ABA Model Rules of Professional Responsibility did in 2012 and that is to add very specific language about attorney competency and technology. In the newly revised Texas Rule 1.01, Paragraph 8, it states:

Because of the vital role of lawyers in the legal process, each lawyer should strive to become and remain proficient and competent in the practice of law, including the benefits and risks associated with relevant technology. Continue reading

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

The New AI Executive: 6 Must-Reads for Legal

The recent Executive Order on artificial intelligence (AI), though directed at federal agencies to prioritize AI investment in research and development, is likely to continue to spur the conversation on use of AI and machine learning in the legal realm.

This is particularly so in e-discovery, where technology-assisted review (TAR), a form of AI, is seeing greater acceptance and refinement in the legal space—that is, helping corporate legal departments take control of review costs and enabling law firms to provide superior and differentiated services to their clients.

But with a deeper understanding of the technology, distinction between TAR 1.0 and TAR 2.0 systems like Catalyst’s Insight Predict (based on the continuous active learning, or CAL, protocol), and advancements to take maximum advantage of TAR techniques on more review tasks, AI can be even more useful and effective in the legal world.    Continue reading

How to Create a Knowledge-Driven Discovery Business—While Containing Costs

Corporate legal department management is quickly changing from a legal to a business process. Legal professionals, along with their business counterparts, are looking critically at how to control costs and meet ever-tightening budgets. Gone are times wistfully referred to by outside counsel as “the salad days,” where the only cost controls law departments put in place were case reserves and ever-expanding litigation budgets. Running the legal department like the rest of the corporate business units is now the rule, not the exception.

With litigation costs—and particularly discovery and document review—comprising larger and larger shares of spend, this is a ripe area to impose cost controls. However, more often than not, even the most forward-thinking in-house legal professionals don’t have the tools or insight to know where they can improve in discovery spend. Rather, most of this information resides with any number of disparate e-discovery vendors and law firms, making it near-impossible to make real-time, data-driven decisions. 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

High-Efficiency Review Enables Client to Meet Short Deadlines; Significantly Reduces Costs

Catalyst Managed Review + TAR 2.0 Finds 94% of the Relevant Documents

Our client, a global corporation, was facing an investigation by a government agency into alleged price fixing. The regulators believed they would find the evidence in the documents and issued a broad request—with just four months to get the job done.

This wasn’t a case of finding a needle in a haystack. Rather, a wide range of documents were responsive. A sample of the initial collection suggested that as many as 45% of the documents would be responsive. One option was to produce everything but the client had a significant amount of confidential and proprietary information that it did not want to be inadvertently exposed. Thus, they needed to produce responsive documents, but only responsive documents. 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

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

Are People the Weakest Link in Technology Assisted Review? Not Really.

In mid-October, our friend Michael Quartararo wrote a post for Above the Law asking whether people were the weakest link in technology-assisted review (TAR). Michael offered some thoughts around whether this may be the case, but he didn’t really answer the question. So, we have to ask:

Why aren’t more people using TAR?

One answer is that there is still a lot of confusion about different types of TAR and how they work. Unfortunately, it appears that Michael’s post may have added to the confusion because he did not differentiate between legacy TAR 1.0 and TAR 2.0. Our first thought was to let the article pass without rejoinder or correction. To our surprise, however, it has been cited and reposted as authoritative by several others. To that end, we want to help clarify a number of Michael’s points. We will quote from his post. Continue reading