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

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 lab...

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Bibliographic Details
Published inarXiv.org
Main Authors Erskine, Jonathan, Clifford, Matt, Hepburn, Alexander, Santos-Rodríguez, Raúl
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 28.03.2024
Subjects
Annotations
Computer Science - Human-Computer Interaction
Computer Science - Learning
Human-computer interface
Labels
Machine learning
Man-machine interfaces
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2403.19339