Summary of An Interactive Human-machine Learning Interface For Collecting and Learning From Complex Annotations, by Jonathan Erskine et al.
An Interactive Human-Machine Learning Interface for Collecting and Learning from Complex Annotations
by Jonathan Erskine, Matt Clifford, Alexander Hepburn, Raúl Santos-Rodríguez
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel human-machine learning interface that allows users to provide annotations using counterfactual examples, complementing traditional binary labels. The goal is to alleviate the assumption that human annotators must adapt to constraints imposed by traditional labeling methods. By allowing for more flexibility in supervision information collection, this approach aims to improve model performance, accelerate learning, and build user confidence. The proposed interface is designed for binary classification tasks and enables users to utilize counterfactual examples as annotations for a dataset. The authors discuss the challenges and potential future extensions of this work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers learn better by letting humans teach them in a more flexible way. Currently, humans are forced to use traditional labels when teaching computers. This can be frustrating and lead to poor results. The researchers propose a new way for humans to help computers learn by using examples that show what is NOT possible (counterfactuals). This makes it easier for humans to teach computers and can improve the accuracy of predictions. |
Keywords
* Artificial intelligence * Classification * Machine learning
