Rockville, MD — Much has been made of the introduction of advanced AI into the world of eDiscovery, especially with the rise of generative AI and the use of large language models (LLMs). One of the most prominent topics is that of LLMs replacing human reviewers for the task of first level review. It’s an exciting proposition and, on the surface, an obvious application.

But we may be skipping some crucial steps by jumping directly to this solution.

If we treat LLMs purely as replacements for human reviewers, we risk underutilizing their true potential while also inviting unnecessary risk. Before we aim to replace, we should focus on augmentation: using LLMs to enhance the efficiency, consistency, and accuracy of human-led review.

There are many augmentation opportunities that could yield outsized value:

• Summarizing complex documents or threads for faster intake;
• Suggesting preliminary relevance assessments for human confirmation;
• Identifying potential privilege triggers for closer attorney review;
• Grouping documents by theme or entity for smarter batching;
• Defining groups of documents for review and informing reviewers what elements they are most likely to evaluate in a set.

These uses don’t eliminate the reviewer, nor the AI.  Instead, they empower both.

By embedding LLMs into thoughtful workflows, we can increase reviewer focus and reduce cognitive strain. By keeping humans in the crucial part of the loop, we can enhance and maximize the benefits of AI operations through more targeted application.  We can also reduce risk overall by making it easier for reviewers to spot anomalies or patterns. The result is a collaborative model where humans provide legal judgment, and AI tools bring clarity to the issues at hand.

In the rush to automate, we shouldn’t overlook the power of enhancement. Human-in-the-loop AI is not a fallback.  It may be the most reliable, defensible, and scalable model for years to come.

Written By Brandon Mack, Counsel and Director of Legal Technology and AI