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Gen AI In e-Discovery: The Now, the Next and the Never
Generative AI (GAI) is reshaping e-Discovery workflows, with technology-assisted review (TAR) evolving from using established continuous active learning (CAL) methods to advanced large language models (LLMs). As this transformation unfolds, understanding precisely what is realistic now, what’s imminent on the horizon, and what remains purely speculative is essential for legal professionals and e-Discovery technologists alike.
The best place to start is with clarity around the current state of GAI e-Discovery technology and adoption, particularly in predictive coding and internal workflows. From there, we can anticipate forthcoming judicial acceptance and expanded applications based on established legal precedents but must critically address exaggerated expectations and recognize the enduring necessity of human judgment in e-Discovery. By integrating technical insights with legal analysis, clear guidance is possible for navigating the realities and limitations of GAI in the evolving landscape of e-Discovery.
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