The recent Executive Order on artificial intelligence (AI), though directed at federal agencies to prioritize AI investment in research and development, is likely to continue to spur the conversation on use of AI and machine learning in the legal realm.

This is particularly so in e-discovery, where technology-assisted review (TAR), a form of AI, is seeing greater acceptance and refinement in the legal space—that is, helping corporate legal departments take control of review costs and enabling law firms to provide superior and differentiated services to their clients.

But with a deeper understanding of the technology, distinction between TAR 1.0 and TAR 2.0 systems like Catalyst’s Insight Predict (based on the continuous active learning, or CAL, protocol), and advancements to take maximum advantage of TAR techniques on more review tasks, AI can be even more useful and effective in the legal world.   

Here are some popular resources that aim to deepen understanding, TAR 2.0 and ever-expanding applications in e-discovery:

eBook: TAR for Smart People

The expanded and updated third edition of the authoritative reference guide on TAR and the newer CAL protocols provides a comprehensive overview of TAR 2.0, cost-savings analysis, best practices for using TAR 2.0 on outbound productions, investigations, opposing party reviews, depo prep and issue analysis, and privilege and privilege QC, and real-life case studies. It has been used as the go-to reference by judges, law firm professors, law firms and in-house counsel. Download for free here.

Article: Are people the Weak Link in Technology-Assisted Review?

Tom Gricks of Catalyst answers “yes,” but it’s different than one might think. The weak link preventing TAR from achieving its true potential is a lack of clarity surrounding the technology—the components, the development and the distinctions. In this article, Tom provides a thorough analysis of these critical areas. Read here ( sign-in required).

Article: Deep Learning and E-Discovery—Fact or Fiction?

In Bloomberg Law, Dr. Jeremy Pickens, Catalyst chief scientist, talks about deep learning’s shortcomings when applied to e-discovery. “There are four key reasons why deep learning amounts to nothing more than hype in e-discovery, and why domain-specific approaches to supervised learning, particularly TAR 2.0 based on CAL, will continue to be far more effective in solving complex legal document review tasks,” he writes. Read the article here.

TAR Talk Podcast: Can You Do Good TAR with a Bad Algorithm?

Should proportionality arguments allow producing parties to get away with poor productions— simply because they wasted a lot of effort due to an extremely bad algorithm? That was a question that Dr. Bill Dimm, founder and CEO of Hot Neuron (the maker of Clustify software), posed in a recent blog post, TAR, Proportionality, and Bad Algorithms (1-NN) and it was the subject of a TAR Talk podcast. This question is critical to e-discovery, and especially relevant to TAR. Listen to our short podcast led by Bill, with participants Mary Mack from ACEDS, and Catalyst founder John Tredennick and Tom Gricks, in a discussion on whether one can do “good” TAR with a bad algorithm. Listen to the short podcast here.

Blog: Moving Beyond Outbound Productions—Using TAR 2.0 for Knowledge Generation and Protection

Lawyers search for documents for many different reasons. TAR 1.0 systems were primarily used to reduce review costs in outbound productions. As most know, modern TAR 2.0 protocols, which are based on CAL can support a wide range of review needs. In our last post, for example, we talked about how TAR 2.0 systems can be used effectively to support investigations.

That isn’t the end of the discussion. There are a lot of ways to use a CAL predictive ranking algorithm to move take on other types of document review projects. Here we explore various techniques for implementing a TAR 2.0 review for even more knowledge generation tasks than investigations, including opposing party reviews, depo prep and issue analysis, and privilege QC. Read the blog here.

Blog: Five Questions to Ask Your E-Discovery Vendor About CAL

In the aftermath of studies showing that CAL is more effective than the first-generation TAR 1.0 protocols, it seems like every e-discovery vendor is jumping on the bandwagon. Despite these claims, there remains a wide chasm between the TAR protocols available on the market today. As a consumer, how can you determine whether a vendor that claims to use CAL actually does? Here are five basic questions you can ask your vendor to ensure that your review effectively employs CAL. Read the blog here.

At the end of the day, with a clear understanding of the technology, TAR promises to significantly enhance document review for more e-discovery tasks, and technological advances will lead to more and more application opportunities.