Parallel AND/OR search for marginal MAP
Radu Marinescu, Akihiro Kishimoto, et al.
AAAI 2020
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.
Radu Marinescu, Akihiro Kishimoto, et al.
AAAI 2020
Helgi I. Ingolfsson, Chris Neale, et al.
PNAS
Romeo Kienzler, Johannes Schmude, et al.
Big Data 2023
Remo Christen, Salomé Eriksson, et al.
ECAI 2023