All posts tagged Da Silva Moore

Stoking a Dead Fire: Da Silva Moore Plaintiffs File Petition for Writ of Certiorari

U.S. Supreme Court

As reported first by Victor Li, a reporter at Law Technology News, the plaintiffs in Da Silva Moore are at it again—this time with a cert petition to the U.S. Supreme Court. In their petition, the plaintiffs contend that appellate courts should review “a judge’s determination that he is not biased under” the de novo standard (followed only by the Seventh Circuit) instead of a “deferential ‘abuse of discretion’” standard (“generally used” by every other court of appeals).

If you’ve successfully forgotten what the drama is all about in this case, let me refresh your recollection: the plaintiffs’ allegations of bias stem from Judge Peck’s “extrajudicial advocacy of predictive coding” (aka: technology/computer assisted review). The plaintiffs submitted in their petition that Judge Peck has “ties to the e-discovery industry and to predictive coding vendors in particular” and has received money from teaching about the benefits of this technology.

Will this case make it to the U.S. Supreme Court? 

Conservatively put by our friends at IT-Lex, “the percentages suggest that it’s unlikely.” In the interim, while we await the next development in this never-ending saga, perhaps we can circle back to what ediscovery is all about: securing the just, speedy, and inexpensive determination of every action and proceeding. 

Related posts:

  • Monica Bay’s recap of Judge Peck’s order refusing to recuse himself can be found here
  • My synopsis of the Second Circuit’s opinion is here

Top 5 Ediscovery Case Summaries – May 2013

Read the very latest ediscovery case law summaries

Below are the top 5 ediscovery case law summaries for May, 2013.

Court Considers Possibility of Clawback Order in Undue Burden Assessment
In re Coventry Healthcare Inc., 2013 WL 1187909 (D. Md. Mar. 21, 2013).

Sanctions Granted for Social Media Spoliation
Gatto v. United Air Lines, Inc., 2013 WL 1285285 (D.N.J. Mar. 25, 2013).

Court Looks to Circumstantial Evidence to Rely on Evidence from MySpace
People v. Kucharski, 2013 WL 1281844 (Ill. App. 2nd Dist. Mar. 29, 2013).

Proportionality is Key Principle in Predictive Coding Case
In re Biomet, 2013 WL 1729682 (N.D. Ind. Apr. 18, 2013).

Da Silva Moore Drama Dissipates
Da Silva Moore v. Publicis Groupe SA, 12-5020 (2d Cir. Apr. 10, 2013).

To check out more ediscovery case summaries, visit our Case Law library.

To the Merits, At Last: Judge Peck Recusal is Denied

ediscovery case law news-Judge Peck recusal denied

Ediscovery case law enthusiasts, gather ‘round – thanks to the Second Circuit Court of Appeals, the Da Silva Moore recusal “sideshow” is finally dead in the water.

As first reported earlier today by the friendly folks at IT-Lex, on April 10, 2013 in an order of notable brevity, Judge Jane A. Restani denied the petitioners’ request for theJudge Peck Recusal is Denied recusal of Judge Andrew J. Peck, stating simply that: “Petitioners have not ‘clearly and indisputably demonstrate[d] that [Magistrate Judge Peck] abused [his] discretion’ in denying their district court recusal motion… or that the district court erred in overruling their objection to that decision.”

At last, the ediscovery community is free of this distracting drama. Cue the music!

Da Silva Moore’s Affect on Ediscovery

The recusal motions that clouded Da Silva Moore created nothing but distraction. Judges, lawyers and providers in the legal ediscovery industry are just as connected as the clients we serve. In ediscovery’s constant state of change, it’s important to remember that ideas precede growth – that leaving room for objective, non-confidential commentary and brainstorming in publications and at conferences is not only appropriate, but imperative for the development of standards, best practices and new technology innovations. Hopefully this short and succinct opinion from the Second Circuit is a clear call that our community’s focus should lie in progressing substantive ediscovery law and practices, not discord and frivolity.

Personally, I look forward to the future of ediscovery jurisprudence from Judge Peck. Like my Kindergarten teacher or childhood dentist, I never want to see him retire. A true trailblazer, in 1995, Judge Peck issued one of the first cases to order the discovery of electronic data (Anti-Monopoly, Inc. v. Hasbro, Inc.), and in 2012 he paved the way for cutting-edge machine learning technology to be used in legal document review through the Da Silva Moore case. What’s next? I can’t wait to discuss more about it with Judge Peck, Ralph Losey, and everyone else in this exceptional legal technology community.

Technology Assisted Review (TAR) Increases Efficiencies, Drives Cost Savings

Technology-Assisted Review (TAR) Increases Efficiencies, Drives Cost Savings

In Da Silva Moore, Judge Peck emphasized, “there simply is no review tool that guarantees perfection.” Perfect production would likely prove unduly burdensome under Rule 26, as the cost of getting it exactly right would likely outweigh any benefits. Pursuant to the Federal Rules requiring the “just, speedy, and inexpensive” resolution of any matter, parties to litigation should aim to drive down costs while maintaining or improving the efficiency of the review process.

As  my previous blog post indicated, when technology-assisted review is properly leveraged, it is a valuable tool that can bolster the review process—but how does such a tool effect the overall efficiency of review, and does a party using it realize substantial cost savings? Let’s take a look.


: Technology-assisted review (TAR) requires additional tasks and expertise, which significantly increases costs upon a party leveraging it.


TAR improves efficiency, which drives substantial cost savings.

Under the manual search and review model, teams of licensed attorneys swoop in, receive training on the case and painstakingly review every document for privilege, relevance, and key issues. These attorneys make document-coding decisions for hours on end, often under tight time frames, typically reviewing 50 to 80 documents an hour. In addition to the reviewers, this model also requires expert attorneys to manage the review process by organizing training sessions, assigning document batches, validating quality control, and preparing final production sets. With the large staff required to perform such a task, it should come as no surprise that document review often accounts for about 73 percent of review costs.

A process leveraging TAR poses a lower staffing need than manual document review, resulting in a more efficient overall process. Typically, only those attorneys considered to be subject matter experts initially review and code fractions of the document set.  These experts “train” the system until the machine achieves a level of confidence, precision and recall acceptable to the case team—resulting in substantially less time spent reviewing the larger document corpus. Once appropriate recall and precision levels are reached, then the team can proceed to final review and production. Ultimately, such a process not only limits the number of eyes needed to perform review, but it also applies advanced criteria which removes a greater number of unresponsive documents from the review set.

So, how does this translate to cost savings? Despite the added third party provider costs and higher billing rates for expert reviewers, research suggests that leveraging TAR produces significant cost savings for the review team. According to the RAND study, cost savings ranged from 20 to 70 percent for large document review projects, and the number of attorney review hours decreased by as much as 80 percent in one matter. Simply put, for matters dealing with higher data volumes—thus requiring a greater number of eyes to manually search and review each document—TAR represents an opportunity for significant cost savings compared to a traditional linear review.


Technology-assisted review is, without a doubt, here to stay. If 2012 was the year that the legal community finally lifted the veil on TAR and acknowledged it, then 2013 seems poised to be the year in which it is fully accepted and adopted by the legal community. Those who cling to the myths associated with TAR are in danger of violating duties to their clients, and potentially wasting significant time and money. In nearly every matter, TAR most certainly deserves a look, and you might even like what you find.  So, what are you waiting for?

Technology Assisted Review (TAR): What Are YOU Waiting For?

Technology-Assisted Review (TAR) Increases Efficiencies, Drives Cost Savings

This time last year, technology-assisted review (TAR) was emerging as the most talked about ediscovery topic in 2012. However, absent a few Ediscovery innovators and mavericks, most members of the legal community—from both the bar and the bench—remained at an impasse with regard to actually using this ground-breaking technology: the judiciary was waiting for counsel to bring TAR before them; counsel was waiting for the judiciary to “bless” TAR (or for their clients to direct its use); and clients were waiting for counsel to green light its use.

What a difference a year makes.

Silence was finally broken in Da Silva Moore v. Publicis Groupe, where U.S Magistrate Judge Andrew Peck approved the use of technology-assisted review in appropriate circumstances. Subsequent opinions, such as Global Aerospace v. Landow Aviation and In re Actos (Pioglitazone) Products Liability Litigation, similarly approved of TAR for complex cases with high data volumes and sufficiently detailed search processes. Moreover, in EORHB, Inc. v. HOA Holdings, a Delaware Chancery Court even directed parties to leverage TAR, requiring them to show the court why TAR would not be appropriate for the pending litigation. These opinions seemingly represented the requisite “blessing” and opened the floodgates for widespread adoption of TAR.

Caution About Using Technology-assisted review?

Despite the science and evidence substantiating the benefits of TAR, many practitioners, while acknowledging TAR’s perceived value, are proceeding with caution due to lingering concerns about costs, accuracy, and defensibility of process. Nonetheless, the rate of adoption and desire to learn about and understand TAR has steadily increased, paving the way for TAR to become the standard in appropriate cases. Simply put, TAR has emerged as a valuable offering that isn’t going away anytime soon.

In order for TAR to be maximized and reach its full potential, the “concerns” expressed about TAR must be exposed for what they truly are: myths. These myths obscure ongoing debate, and they must be debunked before TAR is fully adopted.

Join us for the next installation in this series as we dive into the most common myths associated with TAR in part two of this three-part series.

Technology Assisted Review: Ask and You Shall Receive

Technology-Assisted Review - Ask and you Shall Receive

Whether it’s a tablet or a smartphone, the latest and greatest technologies are the hottest items on everyone’s 2012 wish list. Like the modern consumer, tech-savvy litigants have long been deliberating the best opportunity to leverage technology-assisted review, or TAR, and other advanced searching technologies with predictive algorithms. Since Da Silva Moore v. Publicis Groupe condoned the use of this technology, practitioners are starting to leverage it.

On the whole, advancing technology and growing data volumes had a profound effect on the ediscovery issues that courts discussed in 2012.

The judiciary devoted significant attention to discovery protocols in 2012.

The increased level of procedural scrutiny was best on display in Da Silva Moore. Specifically, U.S. Magistrate Judge Andrew Peck and U.S. District Court Judge Andrew Carter noted that their primary concern was the defensibility of the method implemented, rather than the “black box” behind the technology. Peck closed by emphasizing that “counsel must design an appropriate process,” leveraging available technologies and appropriate quality control testing.

Many sanctions in 2012 stemmed from counsel trying to keep pace with the “big data” era.

The total number of cases addressing sanctions dropped approximately 10 percent in 2012, but it was still the most discussed topic. For example, in Coquina Invs. v. Rothstein, over 200 defense attorneys collecting, reviewing and producing ESI constituted “a case of too many cooks spoiling the broth” amounting to insufficient production, a finding of gross negligence, and sanctions in the form of attorney’s fees and costs. Coquina and similar cases, should serve as cautionary tales displaying the importance of understanding a client’s data before attempting to preserve or collect it.

Courts were all over the map regarding appropriate ediscovery preservation standards in “big data”.

In Chin v. Port Auth. of New York & New Jersey, for example, the court diverged from the Zubulake standard, finding that counsel’s failure to institute a litigation hold did not constitute negligence per se. Instead, the court in Chin favored a case-by-case, factor-based approach to determine whether spoliation occurred. However, many opinions stuck with the Zubulake standard, such as Voom Holdings LLC v. Echostar Satellite LLC, in which the court found the defendant’s failure to issue a litigation hold to suspend deletion of e-mails constituted gross negligence and warranted severe sanctions. As data volumes continue to proliferate, expect courts to evolve their litigation hold procedures for years to come.

So what’s up next for ediscovery? Will technology-assisted review go mainstream? Will “big data” continue to cause big headaches for practitioners? Find out tomorrow as we conclude our three-part series with a look toward 2013’s hottest topics

Read part three of our year in review series.

Mainstreaming Technology Assisted Review

Mainstreaming Technology Assisted Review

Sorry hipsters, technology-assisted review (TAR) is now mainstream.

In 2012 there have been four cases in which technology-assisted review has been in judicially endorsed in some form or another:

  • Da Silva Moore v. Publicis Group (acknowledging party agreement to use TAR)
  •  In re Actos (Pioglitazone) Products Liability Litigation (instructing parties to meet and confer on how to implement TAR)
  • Global Aerospace v. Landow Aviation (ordering the use of TAR over plaintiff’s objections)
  • EORHB, Inc., et al. v. HOA Holdings, LLC (ordering the use of TAR on the court’s own initiative)

And for the not-so-early adopters? While there’s much catching up to do, here are a few choice tips that will help you build your baseline knowledge.

The Vernacular of Technology-Assisted Review

The ediscovery community loves this technology… it just doesn’t know what to call it. Here’s a smattering of the many terms floating around: “technology assisted review” (TAR), “computer assisted review” (CAR), “automated document review,” and “predictive priority.” If you’re feeling particularly vocal about your preferred jargon, if your TAR is sticky puns are just too good to lose, or if you just like clicking things, be sure to make your voice heard on the TAR v. CAR acronym poll hosted by our friends at the e-discovery team. We will get to a standard term eventually–for now, the important things are: 1) the technology behind the lingo and 2) that you, your firm, your adversary, and the court actually know what you mean when you say, for example, “computer assisted review.”

Machine Learning

I know—it’s a little scary, but machine learning is a key component of TAR. Machine learning uses advanced predictive algorithms and analytics to supplement the judgment of expert human reviewers regarding the responsiveness and non-responsiveness (or other defined category) of documents. Why won’t your TAR case fall victim to the HAL 9000 of document review you ask? Because your brightest subject matter experts will train the machine and iterative quality control checks will ensure that everything is going as planned. Bottom line: it’s a delicate dance—let the computers do what they do best and let the humans do what they do best.

Predictive Algorithms

If you’ve dabbled in consumer technology in the past 10 years, I have good news for you: the predictive component of TAR is not new. In fact, Netflix, and online advertisements (to name a few) all employ algorithms that use your input (e.g., movies watched or products purchased) to recommend future movies or products that you might enjoy. TAR uses this same basic premise.

Key Terminology

Testing and Quality Control procedures are critical when implementing TAR. Using the following metrics, we can “score” the effectiveness of the TAR results to accept a project as complete, or to know when to keep TARing. A good key word search process will use the same methodology.

  1. Recall – a measure of completeness (actually relevant documents retrieved/total actually relevant documents)
  2. Precision – a measure of exactness or how “dirty” your search is (actually relevant documents retrieved/ total retrieved documents).

Here’s a no-frills example (assume the black box represents the documents your search identifies as responsive):

Technology-assisted review image