Category Archives: Predictive Ranking

Video: Understanding Yield Curves in Technology Assisted Review

blog_contextual_diversity_videoIn information retrieval science and e-discovery, yield curves (also called gain curves) are graphic visualizations of how quickly a review finds relevant documents or how well a technology assisted review tool has ranked all your documents.

This video shows you how they work and how to read them to measure and validate the results of your document review. Continue reading

Video: How Contextual Diversity in TAR 2.0 Keeps You from Missing Key Pockets of Documents

blog_contextual_diversity_videoHow do you know what you don’t know when using technology assisted review? As I discussed in a recent post, this is a classic problem when searching a large volume of documents. You could miss documents, topics or terms in a collection simply because you don’t know to search for them.

Contextual Diversity is the solution to that problem. A proprietary TAR 2.0 tool built into Insight Predict, it continuously and actively explores unreviewed documents for concepts or topics that haven’t been seen, ensuring you’ve looked into all corners of the collection. Continue reading

Why Control Sets are Problematic in E-Discovery: A Follow-up to Ralph Losey

Why_Control_Sets_are_Problematic_in_E-DiscoveryIn a recent blog post, Ralph Losey lays out a case for the abolishment of control sets in e-discovery, particularly if one is following a continuous learning protocol.  Here at Catalyst, we could not agree more with this position. From the very first moment we rolled out our TAR 2.0, continuous learning engine we have not only recommended against the use of control sets, but we actively decided against ever implementing them in the first place and thus never even had the potential of steering clients awry.

Losey points out three main flaws with control sets. These may be summarized as (1) knowledge Issues, (2) sequential testing bias, and (3) representativeness. In this blog post I offer my own take and evidence in favor of these three points, and offer a fourth difficulty with control sets: rolling collection. Continue reading

Infographic: A TAR is Born: The Making of a Superstar

Infographic_A_TAR_is_BornE-discovery review has come a long way in a short time. Not long ago, manual, linear review was the norm. Then came keyword search, which helped increase efficiency but was imperfect in its results. Technology-assisted review was a great leap forward, but early TAR 1.0 versions were rigid and slow. Only with the arrival of TAR 2.0 and Continuous Active Learning did TAR finally save the day for e-discovery.

The brief history of how TAR evolved is depicted in a new Catalyst infographic, A TAR is Born: The Making of a Superstar.  See how e-discovery review matured from a demanding infant to a Ph.D. in savings. After you check out the infographic, read much more about TAR in Catalyst’s free e-book, TAR for Smart People.

View Infographic >

Case Study Details How a Major Bank Used Catalyst’s Insight Predict to Cut Its Production Review by 94%

In his 2015 opinion in Rio Tinto PLC v. Vale SA, Magistrate Judge Andrew Peck extolled the benefits of technology assisted review using Continuous Active Learning. In particular, he noted that CAL reduces or even eliminates the need for the rigid seed set required by older TAR methods.

CAL’s flexibility on seed sets was illustrated in a case where a large banking institution alleged it lost millions due to a borrower’s accounting fraud. In response to the borrower’s production request, the bank faced review of 2.1 million documents, even after culling. With neither the time nor budget to review them all, the bank turned to Catalyst’s Insight Predict, the first commercial TAR engine to use CAL. Predict cut the review by 94%.

Read the case study to see how it was done >>

Another Federal Decision Acknowledges that TAR Beats Manual Review

In the annals of case law about e-discovery and technology assisted review (TAR), Malone v. Kantner Ingredients will be only a footnote. In fact, were it not for a footnote, the case would barely warrant mention here.

This blog has chronicled the increasing judicial acceptance of TAR, starting with U.S. Magistrate Judge Andrew J. Peck’s seminal 2012 opinion in Da Silva Moore v. Publicis Groupe, which was the first to approve TAR, and continuing through to Judge Peck’s recent opinion in Rio Tinto PLC v. Vale SA, which declared, “the case law has developed to the point that it is now black letter law that where the producing party wants to utilize TAR for document review, courts will permit it.” Continue reading

Killing Two Birds With One Stone: Latest Grossman/Cormack Research Shows that CAL is Effective Across Multiple Issues

No actual birds were harmed in the making of this blog post!

Since the advent of Technology Assisted Review (aka TAR, predictive coding or computer-assisted review), one of the open questions is whether you have to run a separate TAR process for each item in a document request. As litigation professionals know, it is rare to have only one numbered request in a Rule 34 pleading. Rather, you can expect to see scores of requests (typically as many as the local rules allow). Continue reading

Using Continuous Active Learning to Solve the ‘Transparency’ Issue in TAR

Technology assisted review has a transparency problem. Notwithstanding TAR’s proven savings in both time and review costs, many attorneys hesitate to use it because courts require “transparency” in the TAR process. 

Specifically, when courts approve requests to use TAR, they often set the condition that counsel disclose the TAR process they used and which documents they used for training. In some cases, the courts have gone so far as to allow opposing counsel to kibitz during the training process itself. Continue reading

Judge Peck’s Latest: TAR Now ‘Black Letter Law’; CAL Reduces Significance of Seed Set

It is has been three years since U.S. Magistrate Judge Andrew J. Peck issued the first court decision approving the use of technology assisted review in e-discovery, Da Silva Moore v. Publicis Groupe & MSL Grp., 287 F.R.D. 182 (S.D.N.Y. 2012) (Peck, M.J.), affd, 2012 WL 1446534 (S.D.N.Y. Apr. 26, 2012). “This Opinion appears to be the first in which a Court has approved of the use of computer-assisted review,” he wrote then.

Magistrate Judge Andrew Peck

Magistrate Judge Andrew Peck

Now, in an opinion released yesterday, Judge Peck says that, in the years since Da Silva Moore, “the case law has developed to the point that it is now black letter law that where the producing party wants to utilize TAR for document review, courts will permit it.” Continue reading

Forbes Interviews Catalyst’s John Tredennick and Mark Noel on Technology Assisted Review

ParnellForbesInterview2

What is the impact of data and technology on the modern law firm and lawyer? This was the question Forbes contributor David J. Parnell set out to answer when he recently interviewed John Tredennick, Catalyst’s founder and CEO, and Mark Noel, Catalyst’s managing director of professional services.

At one point in the wide-ranging interview — which was published on Forbes last week — Parnell asks Tredennick about some of the major changes in legal technology he has witnessed over the years. In response, Tredennick says that the legal industry is currently in the midst of a major transition with respect to technology assisted review.

Suddenly technology has come where you take a million documents in review—and for any big firm lawyer that’s a big smile on their face because with junior associates reviewing at 500 docs a day, you’ve got your year made—and somebody comes along and says, “You know, with a wave of a wand and a couple training docs, we’re going to cut that million documents down to about 50,000 docs that are probably important.” Maybe 95% of those billable hours go away. That does not make the lawyers smile. That does not make you smile.

But I’ve seen this for 30 years. The innovation comes out; the billable hour suffers; but you always have a few law firms that are not at the top, and then they say, “We don’t have 50,000 associates. Let’s go outsmart them. We’ll lead the way.” And they start taking business away from the big guys. And the corporate entities listen and change happens.

Elsewhere in the interview, Noel tells Parnell that technologies such as TAR are helping to ease the work of lawyers, but will never replace them.

Many people are realizing that they have to change the way they work. And tools like technology assisted review are changing the way attorneys work. But it’s not going to replace them. TAR tools can quickly analyze millions of documents for subtle patterns, but only humans can decide what’s important to the case, or what stories the documents can tell. So these systems are hybrids: The machines do what they do best, and the humans do what they do best. There will be plenty of work to go around for skilled practitioners who know the tools and have the right skillsets.

Read the full interview at Forbes.