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With the rapid advancement of machine learning models for NLP tasks,collecting high-fidelity labels from AI models is a realistic possibility.Firms now make AI available to customers via predictions as a service (PaaS).This includes PaaS products for healthcare. It is unclear whether these labelscan be used for training a local model without expensive annotation checking byin-house experts. In this work, we propose a new framework for Human Correctionof AI-Generated Labels (H-COAL). By ranking AI-generated outputs, one canselectively correct labels and approach gold standard performance (100% humanlabeling) with significantly less human effort. We show that correcting 5% oflabels can close the AI-human performance gap by up to 64% relativeimprovement, and correcting 20% of labels can close the performance gap by upto 86% relative improvement.

H-COAL: Human Correction of AI-Generated Labels for Biomedical Named Entity Recognition