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
I have noticed that in certain popular document-based systems in the e-discovery marketplace, there is a feature (a capability) that often gets touted. Although I am a research scientist at Catalyst, I have been on enough sales calls with my fellow Catalyst team members to have heard numerous users of document-based systems ask whether or not we have the capability to automatically remove common headers and footers from email. There are document-based systems that showcase this capability as a feature that is good to have, so clients often include it in the checklist of capabilities that they’re seeking.
This leads me to ask: Why?
For the longest time, this request confused me. It was a capability that many have declared that they need, because they saw that it existed elsewhere. That leads me to want to discuss the topic of holistic thinking when it comes to one’s technology assisted review (TAR) algorithms and processes. 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
In the latest episode of the podcast Digital Detectives, hosts Sharon Nelson and John Simek interview Catalyst founder and CEO John Tredennick about technology assisted review (TAR). They discuss exactly what TAR includes and the specific ways it has helped companies reduce discovery costs. They also talk about his recent book, TAR for Smart People.
Listen to the full show by clicking on the link above or by visiting the Legal Talk Network.
Happy holidays to all of our readers and best wishes for amazing discoveries in the New Year!
Are you up-to-date on current topics in e-discovery? Here is your chance to get caught up. We’ve crunched the numbers and compiled this selection of our most popular webinar recordings, based on numbers of views. They cover topics ranging from technology assisted review to legal ethics to protecting privilege.
Many vendors these days claim that their technology-assisted review products use a protocol called Continuous Active Learning. But how can you, as an e-discovery professional, be certain about those claims?
The reason this matters is that CAL has been scientifically proven to be superior to other forms of TAR. In a landmark, peer-reviewed study published last year, e-discovery pioneers Maura Grossman and Gordon Cormack demonstrated CAL’s superiority. Their research proved that CAL was far more effective at finding relevant documents (at a much lower cost) than the one-time training methods used by earlier, TAR 1.0 systems. Continue reading
Today is the first-ever E-Discovery Day, celebrating e-discovery’s vital and growing role in the legal process. The day — which is also the day new e-discovery rules take effect in the federal courts — features a full slate of free videocast panels with some of the top professionals in the field addressing a range of cutting-edge topics.
Catalyst is among today’s presenters, with a program at 4 p.m. Eastern time, Using Advanced Analytics Techniques to Meet the Proportionality Requirements of the New Federal Rules. We’ll discuss how advanced analytics techniques such as timelines, visual analytics, faceted search and the latest technology assisted review protocols can help address proportionality requirements while saving on e-discovery costs in the bargain. 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