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

TL;DR

This paper presents an AI-assisted data labeling interface that uses semi-supervised learning to improve labeling speed and accuracy in complex multi-label scenarios, validated through a user study.

Contribution

Introduces a semi-supervised AI labeling assistant that recommends labels and narrows decision space, enhancing efficiency in complex data labeling tasks.

Findings

AI assistance improves labeling accuracy.

AI assistance increases labeling speed.

Reduced label space benefits labeler performance.

Abstract

Labeling data is an important step in the supervised machine learning lifecycle. It is a laborious human activity comprised of repeated decision making: the human labeler decides which of several potential labels to apply to each example. Prior work has shown that providing AI assistance can improve the accuracy of binary decision tasks. However, the role of AI assistance in more complex data-labeling scenarios with a larger set of labels has not yet been explored. We designed an AI labeling assistant that uses a semi-supervised learning algorithm to predict the most probable labels for each example. We leverage these predictions to provide assistance in two ways: (i) providing a label recommendation and (ii) reducing the labeler's decision space by focusing their 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 and speed, especially when the labeler finds the correct label in the reduced label space. We discuss findings related to the presentation of AI assistance and design implications for intelligent labeling interfaces.