Comparative Advantage of Humans versus AI in the Long Tail

AEA Papers and Proceedings(2024)

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Abstract
Machine learning algorithms now exceed human performance on several 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 is 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.
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