The Science of Rejection: A Research Area for Human Computation

This work identifies a foundational issue in ML for improving model reliability, but it is incremental as it builds on existing rejection learning concepts.

The paper argues that learning to reject model predictions is a central problem in machine learning and proposes that human computation should play a leading role in addressing this challenge, though no specific results or numbers are provided.