Predictive Coding Technologies for the Modern Lawyer

Gone are the days of purely manual document review. Today, lawyers are accustomed to practicing within a digital world built on troves of electronically stored information (ESI). As our modern world becomes even more reliant on the Internet of Things, discovery of ESI can result in wading through terabytes of data. Faced with this and the Federal Rules of Civil Procedure’s mandate “to secure the just, speedy, and inexpensive determination of every action and proceeding,” there has been an increasing acceptance of advanced e-discovery techniques by courts and practitioners.

Perspectives on Predictive Coding and Other Advanced Search Methods for the Legal Practitioner, edited by Jason R. Baron, Michael D. Berman, and Ralph C. Losey, is a definitive resource for lawyers looking to understand where e-discovery stands today and where the field may be headed in the near future. For those specialists who operate within the e-discovery industry, the book details the latest technology and how courts are responding. As one of the book’s editors noted, “[t]oday, every lawyer conducting ‘discovery’ in civil litigation needs to confront the fact that—no matter how large or small the case may be—it is insufficient to simply define the search task as being limited to finding relevant documents in traditional paper files. The legal profession lives and breathes in a world of [ESI]. . . .”

Each of the book’s three editors graduated from law school in 1980, so they have witnessed firsthand how discovery practice has transformed from searching through paper files to utilizing automated technology tools and continuous machine learning to expedite the review process. They have spent over a decade engaged in researching, writing, and teaching about how lawyers can search electronic evidence using smarter methods than manual and keyword searching.

The book is a substantial 20 chapters by various authors, divided into four parts. In the first part, the book provides preliminary observations about the limitations of traditional e-discovery search techniques, such as keyword searching and deduplication. The authors then explore the emerging acceptance of technology-assisted review (TAR) by the courts, highlighting key cases along the way.

In the second part, the book explains the differences among various automated tools and methods that are grouped within the TAR label, the differences between passive and active machine-learning techniques, and the differences between simple versus continuous machine learning. A framework is also provided for developing customized predictive coding workflows. Notably, various perspectives are presented, including from the federal and state courts, the plaintiffs’ and defendants’ bar, and small and large cases.

In the third part, leading experts in the burgeoning field of information retrieval explain the metrics that lawyers need to understand to be able to measure and evaluate the effectiveness of predictive coding and other advanced search technologies. The authors explore quantitative, statistical tools that function to ensure and demonstrate a high-quality and cost-effective review, while also presenting practitioner guides for measuring cost-for-completion tradeoffs while using advanced search technologies. Along the way, the authors also explore existing e-discovery standards, why there are so many competing standards efforts when it comes to predictive coding, and what kind of standards operating outside the federal rules could be applied to e-discovery practice.

In the fourth and final part, the book explores how advanced search methods are being integrated into aspects of legal practice outside of litigation. For example, the authors explore the use of predictive analytics in mergers, acquisitions, and divestitures and its use in overall information governance. In the final chapter, author Kathryn Hume explores how the near future may involve even more advanced search methods springing from the field of deep learning and neural networks,”a type of machine learning using multiple computing layers designed to mimic the brain.

Comment 8 to Model Rule of Professional Conduct 1.1—which relates to the “competence” of counsel—states that “a lawyer should keep abreast of changes in the law and its practice, including the benefits and risks associated with relevant technology[.]” Thus, the litigator’s obligation to keep up with developments in e-discovery and information search technology is not optional. Perspectives on Predictive Coding and Other Advanced Search Methods for the Legal Practitioner is a new resource for cutting-edge electronic discovery technology that every lawyer needs.