H-COAL: Human Correction of AI-Generated Labels for Biomedical Named
  Entity Recognition
Xiaojing Duan John P. Lalor
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 labels
can be used for training a local model without expensive annotation checking by
in-house experts. In this work, we propose a new framework for Human Correction
of AI-Generated Labels (H-COAL). By ranking AI-generated outputs, one can
selectively correct labels and approach gold standard performance (100% human
labeling) with significantly less human effort. We show that correcting 5% of
labels can close the AI-human performance gap by up to 64% relative
improvement, and correcting 20% of labels can close the performance gap by up
to 86% relative improvement.