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Found 6 papers, 4 papers with code
Annotator Response Distributions as a Sampling Frame
Annotator disagreement is often dismissed as noise or the result of poor annotation process quality.
Subjective Crowd Disagreements for Subjective Data: Uncovering Meaningful CrowdOpinion with Population-level Learning
Human-annotated data plays a critical role in the fairness of AI systems, including those that deal with life-altering decisions or moderating human-created web/social media content.
Disagreement Matters: Preserving Label Diversity by Jointly Modeling Item and Annotator Label Distributions with DisCo
Annotator disagreement is common whenever human judgment is needed for supervised learning.
Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensive
For (2), we introduce a first-of-its-kind dataset of vicarious offense.
Improving Label Quality by Jointly Modeling Items and Annotators
We propose a fully Bayesian framework for learning ground truth labels from noisy annotators.
Neighborhood-based Pooling for Population-level Label Distribution Learning
Supervised machine learning often requires human-annotated data.
