In the user interface (UI) and user experience (UX) world, one of the ways people design successful software is through the creation of a “mental model” of the underlying processes. Mental models have been around since the 1940s and used for different processes but the concept caught hold in software because it gave designers a framework to understand user needs and the problems they were trying to solve.
According to one of the pioneers of Internet usability, Jacob Neilson, “mental models are one of the most important concepts in human-computer interaction.” We use them to inform our software design and we wanted to share one that we created to model the e-discovery process. Continue reading
Last March, we wrote about U.S. Magistrate-Judge Andrew J. Peck’s decision in Rio Tinto PLC v. Vale SA (S.D. N.Y. March 3, 2015). The decision focused on the types of disputes over process that can arise when parties negotiate a TAR 1.0 protocol. In that post, we noted with approval Judge Peck’s acknowledgment that one common bone of contention in TAR 1.0 negotiations ⎯ transparency around training and the seed set ⎯ becomes less of an issue when the TAR methodology uses continuous active learning.
If the TAR methodology uses ‘continuous active learning’ (CAL) (as opposed to simple passive learning (SPL) or simple active learning (SAL)), the contents of the seed set is much less significant.
After issuing his opinion, and doubtless facing continuing squabbles among the parties, Judge Peck appointed Maura Grossman to serve as a special master to resolve discovery disputes relating to the parties’ use of TAR. Several months later, she issued a “Stipulation and Order re: Revised Validation and Audit Protocols for the Use of Predictive Coding in Discovery,” which is the subject of this blog post. Continue reading
Can keyword search be as or more effective than technology assisted review at finding relevant documents?
A client recently asked me this question and it is one I frequently hear from lawyers. The issue underlying the question is whether a TAR platform such as our Insight Predict is worth the fee we charge for it.
The question is a fair one and it can apply to a range of cases. The short answer, drawing on my 20-plus years of experience as a lawyer, is unequivocally, “It depends.” Continue reading
Whether at Sedona, Georgetown, Legaltech or any other of the many discovery conferences one might attend, a common debate centers on the efficacy of keyword search. “Keyword search is dead,” some argue, touting the effectiveness of the newer predictive analytics engines. “Long live keyword search,” comes back in return from lawyers who have relied on it for decades both to find legal precedent and, more recently, relevant documents for their cases.
Often, the critics of keyword searching cite the 1985 Blair and Maron study for the Association of Computing Machinery that suggested that full-text retrieval systems brought back only 20 percent of the relevant documents. That assertion is true but I wonder how many of the debaters have ever read the study itself. My guess is not many, including me. So I decided to give it a read. Continue reading
Recently, Bob Ambrogi, our director of communications, published a post called “Our 10 Most Popular Blog Posts of 2015 (So Far).” To my surprise, one of my 2011 posts topped the list: “Shedding Light on an E-Discovery Mystery: How Many Documents in a Gigabyte?” Another on the same topic ranked fourth: “How Many Documents in a Gigabyte? An Updated Answer to that Vexing Question.”
Hmmm. Clearly, a lot of us are interested in knowing the answer to this question. I have received a number of comments on both posts (both in writing and in conversation), which always makes the writing worthwhile. The RAND people told me they also found my findings of interest when they were putting together their study on e-discovery costs. Continue reading
A key debate in the battle between TAR 1.0 (one-time training) and TAR 2.0 (continuous active learning) is whether you need a “subject matter expert” (SME) to do the training. With first-generation TAR engines, this was considered a given. Training had to be done by an SME, which many interpreted as a senior lawyer intimately familiar with the underlying case. Indeed, the big question in the TAR 1.0 world was whether you could use several SMEs to spread the training load and get the work done more quickly.
SME training presented practical problems for TAR 1.0 users—primarily because the SME had to look at a lot of documents before review could begin. You started with a “control” set, often 500 documents or more, to be used as a reference for training. Then, the SME needed to review thousands of additional documents to train the system. After that, the SME had to review and tag another 500 documents to document effectiveness of the training. All told, the SME could expect to to look at and judge 3,000 to 5,000 or more documents before the review could start. Continue reading
I do not know if any leprechauns appeared in this case, but the Irish High Court found the proverbial pot of gold under the TAR rainbow in Irish Bank Resolution Corp. vs. Quinn—the first decision outside the U.S. to approve the use of Technology Assisted Review for civil discovery.
The protocol at issue in the March 3, 2015, decision was TAR 1.0 (Clearwell). For that reason, some of the points addressed by the court will be immaterial for legal professionals who use the more-advanced TAR 2.0 and Continuous Active Learning (CAL). Even so, the case makes for an interesting read, both for its description of the TAR process at issue and for its ultimate outcome. Continue reading
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
I have been on the road quite a bit lately, attending and speaking at several e-discovery events. Most recently I was at the midyear meeting of the Sedona Conference Working Group 1 in Dallas, and before that I was a speaker at both the University of Florida’s 3rd Annual Electronic Discovery Conference and the 4th Annual ASU-Arkfeld E-Discovery and Digital Evidence Conference.
In my travels and elsewhere, I continue to see a marked increase in talk about the new TAR 2.0 protocol, Continuous Active Learning (CAL). I have been seeing increasing interest in CAL ever since the July 2014 release of the Grossman/Cormack study, “Evaluation of Machine-Learning Protocols for Technology-Assisted Review in Electronic Discovery.” Continue reading
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