Systems and methods for classifying electronic information are provided by way of a Technology-Assisted Review (“TAR”) process, specifically an “Auto-TAR” process that limits discretionary choices in an information classification effort, while still achieving superior results. In certain embodiments, Auto-TAR selects an initial relevant document from a document collection, selects a number of other documents from the document collection and assigns them a default classification, trains a classifier using a training set made up of the selected relevant document and the documents assigned a default classification, scores documents in the document collection and determines if a stopping criteria is met. If a stopping criteria has not been met, the process sorts the documents according to scores, selects a batch of documents from the collection for further review, receives user coding decisions for them, and re-trains a classifier using the received user coding decisions and an adjusted training set.