How much can you save using TAR 2.0, the advanced form of technology assisted review used by Catalyst’s Insight Predict? That is a question many of our clients ask, until they try it and see for themselves.
Perhaps you’ve wondered about this. You’ve read articles or web sites talking about TAR’s ability to lower review costs by reducing the numbers of documents requiring review. You might even have read about the even-greater gains in efficiency delivered by second-generation TAR 2.0 platforms that use the continuous active learning protocol. But still you’ve held out, maybe uncertain of the technology or wondering whether it is right for your cases. Continue reading
Deep learning. The term seems to be ubiquitous these days. Everywhere from self-driving cars and speech transcription to victories in the game “Go” and cancer diagnosis. If we measure things by press coverage, deep learning seems poised to make every other form of machine learning obsolete.
Recently, Catalyst’s founder and CEO John Tredennick interviewed Catalyst’s chief scientist, Dr. Jeremy Pickens (who we at Catalyst call Dr. J), about how deep learning works and how it might be applied in the legal arena.
JT: Good afternoon Dr. J. I have been reading about deep learning and would like to know more about how it works and what it might offer the legal profession. Continue reading
This week’s ransomware attack against DLA Piper, one of the nation’s largest law firms, provided a harsh reminder of the need for lawyers and law firms to be vigilant about cybersecurity. In DLA Piper’s case, the firm’s security system detected suspicious activity and its IT team acted quickly to isolate the malware, according to a statement, but as of yesterday, the firm was still working to restore full operations.
A ransomware attack against a global law firm is a major intrusion, but it is important to remember that such attacks often begin with a single malicious email and can happen to law firms of any size. Opening a malicious attachment or clicking a malicious link can plant the ransomware virus and allow it to propagate throughout a firm. Continue reading
Chief legal officers have a mandate to reduce costs and manage compliance. But how do they do that in the context of litigation and e-discovery?
On Wednesday, June 28, in San Francisco, Bloomberg Law Big Law Business and Catalyst will present a live program, Successful Legal Department Management: Innovation to Control Litigation Costs and Ensure Compliance.
Designed for general counsel, corporate counsel and leaders of corporate legal departments, this program will discuss strategies and technologies for a successful litigation department. Continue reading
The Texas Supreme Court issued a major e-discovery opinion this week, using a discovery dispute between homeowners and their insurer State Farm Lloyds to provide broad guidance for Texas litigants and judges on how to resolve disagreements over the form of production of electronically stored information.
The court did not decide the appropriate form of production in this case, choosing instead to send the case back to the trial court for the parties to reargue the issue with the “benefit of the guidance we seek to provide today.” However, it used the occasion to emphasize that proportionality is the key determinant and it laid out factors for courts to consider in balancing litigants’ competing interests on a case-by-case basis. Continue reading
Machine learning is an area of artificial intelligence that enables computers to self-learn, without explicit programming. In e-discovery, machine-learning technologies such as technology assisted review (TAR) are helping legal teams dramatically speed document review and thereby reduce its cost. TAR learns which documents are most likely relevant and feeds those first to reviewers, typically eliminating the need to review from 50 to 90 percent of a collection.
Lawyers are getting it, as evidenced by their expanding use of TAR. At Catalyst, 50 percent of matters now routinely use TAR—and none have been challenged in court. Continue reading
For some time now, critics of technology assisted review have opposed using general recall as a measure of its effectiveness. Overall recall, they argue, does not account for the fact that general responsiveness covers an array of more-specific issues. And the documents relating to each of those issues exist within the collection in different numbers that could represent a wide range of levels of prevalence.
Since general recall measures effectiveness across the entire collection, the critics’ concern is that you will find a lot of documents from the larger groups and only a few from the smaller groups, yet overall recall may still be very high. Using overall recall as a measure of effectiveness can theoretically mask a disproportionate and selective review and production. In other words, you may find a lot of documents about several underlying issues, but you might find very few about others. Continue reading
Redaction is a necessary evil of e-discovery review. While essential to protecting privileged and confidential information, it can be cumbersome and time-consuming to go through a document and draw black boxes over individual words and phrases.
That’s why a new feature in Catalyst Insight should be welcome news to document reviewers everywhere. Called Automated Redaction, it is a major enhancement of Catalyst Insight’s redaction tool, enabling users to make redactions with documents more easily, quickly make multiple redactions, and better QC redactions after they’re made. Continue reading
Since 2011, I have been sampling our document repository and reporting about file sizes in my “How Many Docs in a Gigabyte” series of posts here. I started writing about the subject because we were seeing a large discrepancy between the number of files per gigabyte we stored and the number considered to be standard by our industry colleagues. Indeed, in 2011, I reported that we were finding far fewer documents per GB (2,500) than was generally thought to be the industry norm, which ranged from 5,000 to 15,000. Continue reading
Citing research on the efficacy of technology assisted review over human review, a federal court has approved a party’s request to respond to discovery using random sampling.
Despite a tight discovery timeline in the case, the plaintiff had sought to compel the defendant hospital to manually review nearly 16,000 patient records. Continue reading