Gone are the days when legal teams would review all documents one by one to respond to a regulatory demand, litigation request, or conduct an internal investigation. Instead, modern legal professionals leverage machine learning and AI technology known as predictive coding to prioritize the identification of relevant documents buried in mountains of big data. Predictive coding with continuous machine learning utilizes human coding decisions (i.e., relevant / not relevant) to actively learn the kinds of document and language patterns that the lawyers are looking for.
Applying predictive coding saves time and money, helps identify the most relevant documents earlier and lowers costs by reviewing far less irrelevant data. After just a few example documents, the AI can begin searching for similar content and identifying documents for human review. Throughout the review process, the system continuously learns more about what is relevant and adjusts its suggested documents for review accordingly, with most likely relevant documents at the top of the queue. Plus, the machine learning model doubles as a quality control, and at the end of a project it can be used to verify human review decisions, analyze related data sets, and correct errors before production.