Attorney Shannon Brown Teaching eDiscovery Technologies Course
Attorney Shannon Brown will teach a course on eDiscovery technologies at Widener University School of Law on January 5 and 6, 2015. The new course, taught by a lawyer with direct technology knowledge, explores the critical, technical aspects of eDiscovery. Most discussions of eDiscovery gloss-over the technical issues. But as this course will demonstrate, the implementation of the technologies themselves carry significant and surprising legal consequences and thus are part of a lawyers legal duty to clients—that is, the lawyer cannot just “outsource” the “technical”aspects to non-lawyers.
The course gains added importance in the wake of the 2013 updates to the Pennsylvania Rules of Professional Conduct which specifically addressed the lawyer’s duty to personally know technology as part of the lawyer’s duty of competence (Rule 1.1) and to personally supervise both associates, internal personnel, and outside personnel (Rule 5.3) on technology issues. (If you are a lawyer practicing in Pennsylvania and have not read these changes, see the 2013 updates to the Pennsylvania Rules of Professional Conduct.)
eDiscovery Technologies Course Summary—Widener Law
Today’s law practice deeply involves competence with information technologies. Lawyers must be familiar with information technologies as part of a lawyer’s ethical duty to clients. Clients also increasingly demand that attorneys manage legal costs by properly applying technological efficiencies.
But at the same time, lawyers face a deluge of electronically stored information (ESI) unlike anything seen before and occurring in even simple litigation. Manually weeding through gigabytes of emails, texts, tweets, geolocation data, social media content, corporate ERP systems data, database content, and document management systems output to respond to eDiscovery requests or to analyze materials received from opponents can quickly overwhelm—or even be impracticable.
Fortunately, artificial intelligence and machine learning software tools now exist that may permit lawyers, in appropriate cases, to efficiently leverage technology to
- significantly reduce legal analysis time
- while quantifying performance and
- maintaining, or enhancing accuracy and integrity.
Knowing what these technologies are, how they work, what the limitations and pitfalls are, and how to deploy them is what this course is all about.
eDiscovery Technologies Course Objectives
This course introduces students to the technical aspects of emerging technology assisted review (TAR) in eDiscovery. TAR includes several technologies such as Boolean keyword search, probability systems, and “predictive coding”/predictive analytics tools. (I, however, prefer the term computer augmented legal analysis (CALA) as the most accurate term.) Students will gain
- practical, law practice insights into the data deluge that drives the need for more efficient legal analysis tools including discussion of data sources, data types, and data collection issues;
- basic project management insights related to the technical aspects of TAR;
- a basic understanding of the primary types of TAR-related tools available including traditional Boolean keyword search, general TAR tools, often confused document management systems, and newer “predictive coding”/predictive analytics / CALA tools;
- ability to distinguish TAR-related tool types, how to select tools, and how to address the strengths and weaknesses of each type;
a technical understanding of how predictive analytics/CALA tools work;
- insights into the metrics used to measure the performance of TAR systems;
- insights into the types of computer equipment currently needed to run the newer TAR tools; and
- hands-on use of a predictive analytics tool while performing a simulated eDiscovery task (to apply the theory discussed in class).
Thus, at the conclusion of the course, the student will be able to evaluate whether eDiscovery tools are appropriate for a case, understand the various types of tools, be able to discuss the strengths or weaknesses of each type, be able to apply the primary metrics associated with TAR, and understand the project flow for a basic case.