Beyond Banker’s Boxes: My Key Take-Aways from Day One of an E-Discovery Conference
Anyone who practiced law before the digital era recalls conference rooms filled with towering stacks of banker’s boxes, teams of exhausted associates poring over reams of paper late into the night, and sticky notes attached to countless documents. Discovery was once a labor-intensive, physical process demanding both human endurance and legal insight. I remember the good old days when a brief discovery conference was all it took—parties would simply agree on a handful of search terms, and that was enough to launch the process. Those simpler times, while straightforward, have given way to a reality where such an approach no longer suffices.
Today, banker’s boxes have been replaced by terabytes of data. Modern discovery now encompasses not only paper documents but also emails, text messages, Teams/Slack logs, linked files, social media posts, metadata, and a vast array of electronically stored information. In many ways, a single smartphone can hold more data than entire law firms once stored in filing cabinets. The exponential growth in data types and volumes—and the inherent complexity of digital communications—renders a few predetermined keywords insufficient to capture all relevant evidence.
The legal profession's initial step toward modernization came with the advent of Technology-Assisted Review (TAR) and predictive coding. These innovations enabled legal teams to process vast collections of electronic documents far more efficiently than manual review ever could. Once trained, machine learning algorithms can sift through data to pinpoint relevant documents, significantly reducing both the time and cost involved in discovery. Now we may be witnessing a further evolution with generative AI tools that could transform the discovery process again. Some suggest that generative AI could even be better than TAR. But it remains to be seen how these approaches will ultimately converge in the discovery landscape. I guess only time will tell.
This rapid technological progress obviously poses significant challenges for judges. Many of us must now make critical decisions regarding discovery disputes that involve technologies with which we may have little familiarity. When attorneys debate the merits of predictive coding protocols or AI-driven search methodologies, we are tasked with evaluating these positions without necessarily having the technical background required for a deep understanding of the issues. The challenge is compounded by inconsistent terminology used by the attorneys and legal technology vendors.
Now I’m not saying that Judges need to become computer scientists, but we must develop a working understanding of these technologies and their implications for discovery. This may involve regular judicial education programs focused on legal technology, the development of standardized terminology for discussing these tools in court, clear guidelines for evaluating the appropriateness and reliability of new discovery methods, and protocols to manage disparities when parties have unequal technological resources.
Improving communication between the legal and technical communities is equally essential. Technologists should strive to explain their tools in clear, nontechnical language, while legal professionals must invest the time necessary to grasp these innovations. Such dialogue is vital to ensure that emerging tools serve the pursuit of justice rather than complicate it.
It is also important to manage expectations in this era of AI-assisted discovery. There is a tendency to fall prey to the “CSI Effect” of e-discovery—the belief that technology can instantaneously resolve any data challenge, as often portrayed in films. In reality, the process is far more complex. When a court orders the production of “all relevant communications” within a specified deadline, it is crucial to recognize the technical work required behind the scenes. IT teams must first locate and retrieve data from various sources such as email servers, cloud storage, local drives, mobile devices, and even legacy systems—a process that can take weeks. The raw data then requires processing into a format that discovery tools can analyze—a task that often involves managing corrupted files, encrypted documents, and proprietary formats. AI and machine learning systems must be carefully configured and trained, with search parameters created, tested, and validated, while rigorous quality control measures are implemented to ensure accuracy and completeness. Additional technical challenges, including issues of encryption, file corruption, or system incompatibility, can further delay the process.
Understanding these realities can help the judiciary craft more realistic discovery orders. Instead of issuing blanket deadlines, judges might consider staged deadlines that acknowledge the technical complexities involved, require parties to disclose their technological capabilities and limitations early in the process, and encourage the development of detailed, feasible discovery plans that account for the necessary time and resources. Pre-agreeing on specific tools and methodologies before beginning discovery could also contribute to a more balanced and efficient process.
The transition from paper-based discovery to AI-assisted review represents more than just a change in tools—it signifies a fundamental evolution in how we approach the discovery process. As judges, we must adapt to this new technological landscape while remaining true to the core principles of fairness, accessibility, and the pursuit of truth. While the era of banker’s boxes is behind us, the challenge of ensuring a just and efficient discovery process remains. Our role is to harness these new technologies in a manner that serves justice, ensuring that advanced tools support, and are integrated with, the human judgment at the heart of the legal system.
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