“Human-in-the-Loop machine learning” refers to the need for human interaction with machine learning systems to improve human performance, machine performance, or both. Most machine learning projects do not have the time or budget for human input on every data point, and so need strategies for deciding which data points are the most important for human review. Ongoing human involvement with the right interfaces expedites the efficient labeling of tricky or novel data that a machine can’t process, reducing the potential for data-related errors.