As technology continues to advance, legal professionals should embrace TAR as an invaluable tool in the search for efficient, accurate, and cost-effective legal document review.
In the October 26, 2023 edition of The Legal Intelligencer, Kelly Lavelle wrote, “Technology-Assisted Review: A Superior Approach in Legal Document Review.”
Technology-assisted review (TAR) has changed how lawyers manage and analyze vast volumes of electronic data in the ever-changing landscape of legal document review. Traditionally, search terms have been the preferred method in the e-discovery process. However, TAR is rapidly emerging as a superior alternative, offering numerous benefits over conventional search terms in legal document review.
Technology-assisted review (TAR) is a process of having computer software electronically classify documents based on input from reviewers to expedite the organization and prioritization of the document collection. As reviewers train the software, it learns to identify and highlight relevant information and ensure quality control accurately. TAR allows reviewers to make decisions rapidly by prioritizing the most critical documents. TAR has been accepted by the U.S. courts since the seminal 2012 decision in Da Silva Moore v. Publicis Groupe & M.S.L. Group, 287 F.R.D. 182 (S.D.N.Y. 2012) and is now viewed as black-letter law.
The landscape of technology-assisted review options includes: TAR 1.0, also known as “predictive coding” or “simple active learning,” TAR 2.0, known as “continuous active learning,” TAR 3.0, which is similar to continuous active learning but applied to cluster centers and TAR 4.0 known as hybrid multimodal I.S.T. (intelligently spaced training) predictive coding.
There are several differences between TAR 1.0 and TAR 2.0, which lie in how the algorithm is trained. TAR 1.0 software training begins by taking a random sample of documents from the entire TAR set. A reviewer then codes those documents, and based on the coding in that set (seed set), the software generates a predictive model that is applied across all relevant documents. In contrast, with TAR 2.0—the more advanced approach, the reviewer and software training occur simultaneously, and the algorithms are continuously learning as the documents are reviewed. TAR 3.0 combines the advantages of TAR 1.0 and TAR 2.0. The key aim of TAR 3.0 is to leverage the features of TAR 2.0 with new techniques that will allow for earlier and smarter identification of relevant documents. TAR 4.0 uses a combination of human and machine functions to efficiently and accurately identify relevant data. TAR 3.0 and 4.0 essentially fall under the TAR 2.0 umbrella.
Legal professionals have shifted toward adopting TAR 2.0 due to various advantages. In particular, continuous active learning has been shown to reach higher levels of recall, identifying a greater number of relevant documents more quickly and with less effort by the reviewer than TAR 1.0. Continuous active learning can also readily accommodate changes in the scope of discovery and rolling document productions since it continues training throughout the review process. A TAR 1.0 algorithm stops training when it reaches stable quality results, regardless of how many documents are subsequently reviewed, and requires manual re-training after each document review cycle is complete. A TAR 2.0 algorithm is trained by every coding decision until the review stops. Because the TAR 1.0 algorithm is fully trained before the review begins, it does not adapt to changes in the scope of discovery, such as the addition of documents in a rolling production or the addition of legal issues involved.
Determining which protocol best fits a particular matter depends on case objectives and requires a more detailed understanding of the various methodologies. Whichever variation is used, there are a number of key benefits to using TAR in document review.
Enhanced Precision and Recall
One of the primary advantages of TAR is its ability to enhance both precision and recall significantly. In the context of TAR, precision is a measure of how often an algorithm accurately predicts a document to be responsive. Essentially, it measures the percentage of documents produced that are actually responsive. Recall is a measure of completeness, referring to the percentage of relevant documents identified within the entire universe of documents.
Traditional search terms rely on specific keywords or phrases, potentially missing relevant documents that do not contain those exact terms. TAR, however, uses sophisticated machine-learning algorithms to identify document patterns. This advanced approach enables TAR to uncover relevant documents that might have been overlooked relying solely on search terms.
Reduced Volume of Irrelevant Documents
Search terms often generate large sets of irrelevant documents, burdening reviewers with time-consuming manual review. TAR minimizes this burden by prioritizing the review of documents most likely to be relevant. TAR learns from the coding decisions applied by the reviewers, prioritizes the most likely relevant documents, and excludes irrelevant ones. For projects where documents do not need to be reviewed before production, technology-assisted review can quickly separate the documents into relevant and irrelevant categories without review, allowing for quick production and substantial time and cost savings.
Improved Consistency and Defensibility
TAR offers a consistent and defensible approach to document review. From the initial approval of its use in Da Silva Moore, TAR has become a staple in modern litigation. Unlike search terms, which can be ambiguous and susceptible to implementation errors, TAR’s machine-learning algorithms apply consistent criteria across all documents. This uniformity not only enhances accuracy of the review process but also bolsters its defensibility in legal proceedings, as the process is well-documented and transparent.
Time and Cost Efficiency
Numerous courts have recognized the cost-saving benefits of technology-assisted review. By using TAR, legal professionals can significantly reduce review time, eliminating the hours spent on manual document review and reducing attorney fees substantially. Further, TAR provides the advantage of early case assessment by prioritizing relevant documents, thereby reducing the overall volume of documents. This proactive approach provides attorneys with the necessary information to make informed decisions about case strategy, settlement negotiations, or the need for additional evidence. Ultimately, TAR saves both time and valuable resources.
The advantages of technology-assisted review over traditional search terms in the modern legal landscape are clear and compelling. TAR not only offers enhanced precision but also drastically reduces the amount of irrelevant documents in the review set. TAR provides improved consistency coupled with significant cost savings. Its adaptability and early case assessment capabilities further solidify its role as a superior approach to document review in e-discovery. As technology continues to advance, legal professionals should embrace TAR as an invaluable tool in the search for efficient, accurate, and cost-effective legal document review.
Kelly A. Lavelle is an associate at Kang Haggerty. She focuses on e-discovery and information management, from preservation and collection to review and production of large volumes of electronically stored information. Contact her at klavelle@kanghaggerty.com.
Reprinted with permission from the October 26, 2023 edition of “The Legal Intelligencer” © 2023 ALM Media Properties, LLC. All rights reserved. Further duplication without permission is prohibited, contact 877-257-3382 or reprints@alm.com.