The Evolution of AI in eDiscovery: Beyond Just TAR
Introduction: More Than Just a Buzzword
We hear buzzwords about technology all the time, and lately, "AI is the new TAR" has become a popular adage in the legal sector. At a surface level, it’s easy to see why this comparison is made; both Technologies Assisted Review (TAR) and Artificial Intelligence (AI) have revolutionized eDiscovery. However, equating them oversimplifies the nuances involved. While TAR was created to address a single, distinct problem, AI encompasses a vast toolbox filled with diverse functionalities. Let’s dive deeper into how these technologies differ and how they can work synergistically to reshape eDiscovery practices.
Understanding TAR: A Focused Approach
TAR’s primary objective is straightforward: to efficiently review massive volumes of documents for relevance. It was designed to streamline this specific process, sacrificing flexibility in favor of speed and efficiency. While some practitioners have made strides in adapting TAR workflows for broader applications, these adjustments often require cumbersome workarounds. Unfortunately, this can lead to compromises in the quality of the results.
In contrast, the capabilities of AI, particularly large language models (LLMs), allow for a more versatile approach to various challenges within eDiscovery. They offer much more than just document classification; they can assist with a range of tasks like assigning issue codes, drafting privilege descriptions, and even document summarization.
AI: A Class of Technologies
It’s essential to recognize that AI is not a monolithic entity but a spectrum of technologies. Large language models, which reside at the forefront, come in various forms—encoders for predictive tasks, decoders for generative tasks, and hybrid models designed for both purposes.
The models serve as only the foundational layer of a broader AI solution. Creating a tailored AI application involves more than just applying a model and generating a prompt. The potential combinations of different models, prompts, workflows, and post-AI refinement processes are nearly limitless, opening doors to innovative and effective eDiscovery strategies.
The Role of Human Expertise
Despite the impressive capabilities of AI, human involvement remains crucial. Whether enhancing a model through specific training or fine-tuning a prompt, human expertise is essential to guide AI outputs toward practical and relevant results.
Additionally, the validation processes embedded in AI workflows are indispensable. A robust human review mechanism should be in place—both during the model’s testing phase and through statistically significant sampling of final outputs. This ensures accuracy, defensibility, and the maximization of AI’s benefits in legal settings.
Crafting Purpose-Fit Solutions
The diverse range of AI solutions creates opportunities for customizing approaches based on the tasks at hand. Whether a legal team requires document classification, summarization, translation, or entity recognition, there is an AI tool designed for each need.
By layering these capabilities over modern analytics already embedded in review platforms, the potential for innovation and efficiency skyrockets. However, this plethora of options may also contribute to an overwhelming sense of complexity.
The Importance of Partnership
Navigating the landscape of AI technologies doesn’t require one to be an expert. With the rapid pace of advancements, keeping up with new models and their respective strengths can be both technically demanding and time-consuming. This is where having a trusted eDiscovery partner comes into play.
Such a partner can demystify the available options, recommend models tailored to specific tasks, and integrate these solutions into efficient and defensible workflows. Their expertise ensures that even legal teams without deep technical knowledge can leverage AI effectively.
Understanding AI as a Tool, Not a Cure-All
AI should not be viewed as a silver bullet capable of solving all challenges within eDiscovery. Rather, it should be recognized for its role as a powerful set of tools that, when strategically paired with human expertise and existing technologies—both AI and non-AI—can drive smarter, faster, and more effective eDiscovery workflows.
In this intricate landscape, understanding AI’s capabilities and limitations empowers legal professionals to adopt intelligent solutions optimized for their specific needs.