Data and Code for: Comparative Advantage of Humans vs AI in the Long Tail

Abstract: Machine learning algorithms now exceed human performance on a number of predictive tasks, generating concerns about widespread job displacement. However, supervised learning approaches rely on large amounts of high-quality labeled data and are designed for specific predictive tasks. Thus, humans may be required for a large number of tasks each of which are not commonly encountered -- the long tail -- because humans can make predictions for a broader range of outcomes and with exposure to much less data. We show that a self-supervised algorithm for chest X-rays, which does not require specifically annotated disease labels, closes this gap even in the long tail of diseases.