Across every industry, engineers and scientists are in a race to clean and structure massive amounts of data for AI. Teams of computer vision engineers use labeled data to design and train the deep learning algorithms that self-driving cars use to recognize pedestrians, trees, street signs, and other vehicles. Data scientists are using labeled data and natural language processing (NLP) to automate legal contract review and predict patients who are at higher risk of chronic illness. The success of these systems depends on skilled humans in the loop, who label and structure the data for machine learning (ML). When data labeling is low quality, an ML model will struggle to learn.