English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Labeling data is an important step in the supervised machine learninglifecycle. It is a laborious human activity comprised of repeated decisionmaking: the human labeler decides which of several potential labels to apply toeach example. Prior work has shown that providing AI assistance can improve theaccuracy of binary decision tasks. However, the role of AI assistance in morecomplex data-labeling scenarios with a larger set of labels has not yet beenexplored. We designed an AI labeling assistant that uses a semi-supervisedlearning algorithm to predict the most probable labels for each example. Weleverage these predictions to provide assistance in two ways: (i) providing alabel recommendation and (ii) reducing the labeler's decision space by focusingtheir attention on only the most probable labels. We conducted a user study(n=54) to evaluate an AI-assisted interface for data labeling in this context.Our results highlight that the AI assistance improves both labeler accuracy andspeed, especially when the labeler finds the correct label in the reduced labelspace. We discuss findings related to the presentation of AI assistance anddesign implications for intelligent labeling interfaces.

Increasing the Speed and Accuracy of Data LabelingThrough an AI Assisted Interface