An Interactive Human-Machine Learning Interface for Collecting and Learning from Complex Annotations
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916182737551360 |
|---|---|
| author | Erskine, Jonathan Clifford, Matt Hepburn, Alexander Santos-Rodríguez, Raúl |
| author_facet | Erskine, Jonathan Clifford, Matt Hepburn, Alexander Santos-Rodríguez, Raúl |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_19339 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Interactive Human-Machine Learning Interface for Collecting and Learning from Complex Annotations Erskine, Jonathan Clifford, Matt Hepburn, Alexander Santos-Rodríguez, Raúl Machine Learning Human-Computer Interaction 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. |
| title | An Interactive Human-Machine Learning Interface for Collecting and Learning from Complex Annotations |
| topic | Machine Learning Human-Computer Interaction |
| url | https://arxiv.org/abs/2403.19339 |