Category Archives: Technology-Assisted Review

Using TAR for Asian Language Discovery

In the early days, many questioned whether technology assisted review (TAR) would work for non-English documents. There were a number of reasons for this but one fear was that TAR only “understood” the English language.

Ironically, that was true in a way for the early days of e-discovery. At the time, most litigation support systems were built for ASCII text. The indexing and search software didn’t understand Asian character combinations and thus couldn’t recognize which characters should be grouped together in order to index them properly. In English (and most other Western languages) we have spaces between words, but there are no such obvious markers in many Asian languages to denote which characters go together to form useful units of meaning (equivalent to English words). 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

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