5 Reasons You Shouldn't Use Crowdsourcing to Label Training Data

Every day, we talk to artificial intelligence practitioners who are either labeling data internally for training AI models, or they're using crowdsourcing (or outsourcing) for the labeling/annotating. Both are bummers; that's why we exist. Recently, we covered the issues with an in-house approach. If you need only simplistic training data--say, categorized images or ranked articles--then maybe a crowdsourcing solution will cut it. But if you need more sophisticated data, you need good tooling--stuff to do bounding boxes, polygons, segmentation masks, pixel-level annotation, semantic segmentation, and so on.