An Interactive Human-Machine Learning Interface for Collecting and Learning from Complex Annotations

This work proposes an interactive human-machine learning interface that enables human annotators to provide complex annotations, such as counterfactual examples, to complement standard binary labels, with the aim of improving machine learning model performance, accelerating learning, and building user confidence.