Category Archives: Technology-Assisted Review

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

Using TAR Across Borders: Myths & Facts

As the world gets smaller, legal and regulatory compliance matters increasingly encompass documents in multiple languages. Many legal teams involved in cross-border matters, however, still hesitate to use technology assisted review (TAR), questioning its effectiveness and ability to handle non-English document collections.  They perceive TAR as a process that involves “understanding” documents. If the documents are in a language the system does not understand, then TAR cannot be effective, they reason.

The fact is that, done properly, TAR can be just as effective for non-English as it is for English documents. This is true even for the complex Asian languages including Chinese, Japanese and Korean (CJK). Although these languages do not use standard English-language delimiters such as spaces and punctuation, they are nonetheless candidates for the successful use of TAR. 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

Just Say No to Family Batching in Technology Assisted Review

Catalyst_Just_Say_No_Family_BatchingIt is time to put an end to family batching, one of the most widespread document review practices in the e-discovery world and one of the worst possible workflows if you want to implement an efficient technology-assisted review (TAR) protocol. Simply put, it is nearly impossible for family batching to be as efficient as document-level coding in all but the most unusual of situations.

We set out to evaluate this relationship with real world data, and found document-level coding to be nearly 25 percent more efficient than family batching, even if you review and produce all of the members of responsive families. Continue reading

Comparing the Effectiveness of TAR 1.0 to TAR 2.0: A Second Simulation Experiment

Catalyst_Simulation_TAR1_vs_TAR2In a recent blog post, we reported on a technology-assisted review simulation that we conducted to compare the effectiveness and efficiency of a family-based review versus an individual-document review. That post was one of a series here reporting on simulations conducted as part of our TAR Challenge – an invitation to any corporation or law firm to compare its results in an actual litigation against the results that would have been achieved using Catalyst’s advanced TAR 2.0 technology Insight Predict.

As we explained in that recent blog post, the simulation used actual documents that were previously reviewed in an active litigation. Based on those documents, we conducted two distinct experiments. The first was the family vs. non-family test. In this blog post, we discuss the second experiment, testing a TAR 1.0 review against a TAR 2.0 review. 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

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

Deep Learning in E-Discovery: Moving Past the Hype

blog_lightbulb_with_flareDeep learning. The term seems to be ubiquitous these days. Everywhere from self-driving cars and speech transcription to victories in the game “Go” and cancer diagnosis. If we measure things by press coverage, deep learning seems poised to make every other form of machine learning obsolete.

Recently, Catalyst’s founder and CEO John Tredennick interviewed Catalyst’s chief scientist, Dr. Jeremy Pickens (who we at Catalyst call Dr. J), about how deep learning works and how it might be applied in the legal arena.

JT: Good afternoon Dr. J. I have been reading about deep learning and would like to know more about how it works and what it might offer the legal profession. Continue reading

Our 10 Most Popular TAR-Related Posts of 2017 (so far)

Catalyst_Top_10_TAR_PostsMachine learning is an area of artificial intelligence that enables computers to self-learn, without explicit programming. In e-discovery, machine-learning technologies such as technology assisted review (TAR) are helping legal teams dramatically speed document review and thereby reduce its cost. TAR learns which documents are most likely relevant and feeds those first to reviewers, typically eliminating the need to review from 50 to 90 percent of a collection.

Lawyers are getting it, as evidenced by their expanding use of TAR. At Catalyst, 50 percent of matters now routinely use TAR—and none have been challenged in court. Continue reading

Does Recall Measure TAR’s Effectiveness Across All Issues? We Put It To The Test

Does_Recall_Measure_TARs_EffectivenessFor some time now, critics of technology assisted review have opposed using general recall as a measure of its effectiveness. Overall recall, they argue, does not account for the fact that general responsiveness covers an array of more-specific issues. And the documents relating to each of those issues exist within the collection in different numbers that could represent a wide range of levels of prevalence.

Since general recall measures effectiveness across the entire collection, the critics’ concern is that you will find a lot of documents from the larger groups and only a few from the smaller groups, yet overall recall may still be very high. Using overall recall as a measure of effectiveness can theoretically mask a disproportionate and selective review and production. In other words, you may find a lot of documents about several underlying issues, but you might find very few about others. Continue reading