Is GenAI a Potential Alternative to Technology-Assisted Review?

According to Doug Austin of eDiscovery Today, generative AI is promising as a potential alternative to technology-assisted review (TAR) for legal document review and eDiscovery. This article addresses the reasons why genAI could become a strong competitor.

GenAI can interpret language subtleties better than TAR, which relies on keywords and may miss nuanced or indirect expressions in legal texts. Because of genAI’s natural language capabilities, users can engage in conversational queries, generate summaries, and refine searches dynamically. This is less cumbersome than TAR’s complex workflows and rigid model configurations.

TAR often requires specific training for different types of cases or data sets, whereas genAI can generalize across various topics without needing separate training sets. GenAI also has the potential to deliver predictive insights by identifying patterns in documents, aiding strategic decisions in legal reviews by estimating relevance, and predicting query outcomes.

In addition, genAI can handle tasks such as summarizing, drafting review protocols, and synthesizing complex documents for case strategy, going beyond TAR’s core functions. Finally, genAI provides greater transparency in document review. GenAI describes how it classifies documents and offers confidence levels for predicting classification. TAR, on the other hand, generally provides a ranking of the documents’ responsiveness but no insight into why it classifies the documents as relevant. 

Despite considering genAI a future alternative to technology-assisted review for eDiscovery, TAR remains favored for its proven reliability, control, and accuracy, all critical for court-compliant document review. TAR is also optimized explicitly for repetitive tasks, while genAI still faces issues with unpredictable “hallucinations” that can undermine its reliability. Although genAI shows potential for a future role in legal document review, for now, TAR remains essential.