Subjective Crowd Disagreements for Subjective Data: Uncovering Meaningful CrowdOpinion with Population-level Learning
This addresses fairness in AI systems by preventing minority view disregard in human-annotated data, though it is incremental as it builds on existing clustering methods.
The paper tackles the problem of meaningful annotator disagreements in subjective data by introducing CrowdOpinion, an unsupervised learning approach that pools similar items using language features and label distributions, achieving evaluation on benchmark datasets with KL-divergence and accuracy measures.
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. Conventionally, annotator disagreements are resolved before any learning takes place. However, researchers are increasingly identifying annotator disagreement as pervasive and meaningful. They also question the performance of a system when annotators disagree. Particularly when minority views are disregarded, especially among groups that may already be underrepresented in the annotator population. In this paper, we introduce \emph{CrowdOpinion}\footnote{Accepted for publication at ACL 2023}, an unsupervised learning based approach that uses language features and label distributions to pool similar items into larger samples of label distributions. We experiment with four generative and one density-based clustering method, applied to five linear combinations of label distributions and features. We use five publicly available benchmark datasets (with varying levels of annotator disagreements) from social media (Twitter, Gab, and Reddit). We also experiment in the wild using a dataset from Facebook, where annotations come from the platform itself by users reacting to posts. We evaluate \emph{CrowdOpinion} as a label distribution prediction task using KL-divergence and a single-label problem using accuracy measures.