Multidimensional Data Reversion: From Search Terms to GenAI—How We Got Here and What We Learned Along The Way

In this episode of Multidimensional Data Reversion, David Yanofsky and Shannon Capone Kirk explore the intersection of data analysis and law, focusing on the use of AI in legal investigations. They discuss the history and application of AI, particularly in accounting fraud and internal investigations, highlighting the Enron dataset as a key resource. The conversation covers the appropriate use of Gen-AI tools in data review, contrasting internal investigations with production reviews, and emphasizes the importance of client consent and a confined data scope. They also delve into the evolution of document review from search terms to continuous active learning and Gen AI, stressing the need for plain language, human expertise, and metrics to validate AI results in legal contexts.