An Interactive Human-Machine Learning Interface for Collecting and Learning from Complex Annotations
Jonathan Erskine, Matt Clifford, Alexander Hepburn, Raul Santos Rodriguez
Abstract
Human-Computer Interaction has been shown to lead to improvements in machine learning systems by boosting model performance, accelerating learning and building user confidence. In this work, we aim to alleviate the expectation that human annotators adapt to the constraints imposed by traditional labels by allowing for extra flexibility in the form that supervision information is collected. For this, we propose a human-machine learning interface for binary classification tasks which enables human annotators to utilise counterfactual examples to complement standard binary labels as annotations for a dataset. Finally we discuss the challenges in future extensions of this work.
BibTeX
@inproceedings{ijcai2024p999,
title = {An Interactive Human-Machine Learning Interface for Collecting and Learning from Complex Annotations},
author = {Erskine, Jonathan and Clifford, Matt and Hepburn, Alexander and Santos Rodriguez, Raul},
booktitle = {Proceedings of the Thirty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-24}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Kate Larson},
pages = {8644--8647},
year = {2024},
month = {8},
note = {Demo Track},
doi = {10.24963/ijcai.2024/999},
url = {https://doi.org/10.24963/ijcai.2024/999},
}