Rater Cohesion and Quality from a Vicarious Perspective

Fri, 01 Nov 2024 00:00:00 +0000

When labeling controversial content (like tweets about politics), people’s own beliefs strongly influence what they find offensive. We used “vicarious annotation”—asking people not just “Is this offensive to you?” but also “Would a Democrat/Republican/Independent find this offensive?” This revealed fascinating patterns: Republicans were the worst at predicting how others would react, and disagreement spiked on hot-button issues like gun control and abortion. Understanding these patterns helps us build content moderation AI that doesn’t just reflect one group’s values while silencing others.

Sarah K. Luger | Tharindu Cyril Weerasooriya

Rater Cohesion and Quality from a Vicarious Perspective