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

arXiv:2403.19339v1 Announce Type: new
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 …

abstract adapt aim annotations arxiv boosting building computer confidence constraints cs.hc cs.lg human human-computer interaction improvements interactive labels learning systems machine machine learning performance systems type work