machine learning in e-discovery

Machine learning in e-discovery involves using advanced algorithms to automatically identify, categorize, and prioritize relevant documents within large sets of digital data during legal investigations. It learns from examples provided by legal experts, improving its ability to distinguish important information from irrelevant material. This process speeds up document review, reduces costs, and increases accuracy by leveraging patterns and insights that might be difficult and time-consuming for humans to spot manually. Ultimately, machine learning helps legal teams efficiently find critical evidence in vast amounts of electronic data.