Understanding Precision, Recall, and Prompt Iterations in Generative AI for E-Discovery

For at least two decades, artificial intelligence has been used in e-discovery to help surface and prioritize review of potentially responsive documents from large document collections. But while technology-assisted review (TAR) has traditionally been driven by AI in the form of supervised machine learning, some vendors and e-discovery professionals are starting to experiment with the use of generative AI in its place. 

So how effective is generative AI for document review in e-discovery? Is it a replacement for traditional TAR or a supplement? Are there other ways in which this rapidly evolving technology can be used in discovery? 

On this week’s LawNext, we are discussing the application of generative AI in e-discovery. To do so, host Bob Ambrogi is joined by three computer and data scientists from Redgrave Data, a consulting firm that specializes in e-discovery and data science. Today’s guests are:

• Dave Lewis, chief scientific officer, who has over three decades of experience in AI and statistics.

• Lenora Gray, data scientist, who has worked for more than 15 years in law firm project management and matter support roles.

• Jeremy Pickens, head of applied science, a pioneer in the fields of collaborative exploratory search and technology assisted review. 

This episode of LawNext is generously made possible by our sponsors. We appreciate their support and hope you will check them out.

• Paradigm, home to the practice management platforms PracticePanther, Bill4Time, MerusCase and LollyLaw; the e-payments platform Headnote; and the legal accounting software TrustBooks.

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