Rater Cohesion and Quality from a Vicarious Perspective

TL;DR

This paper investigates how vicarious annotation and demographic-aware metrics can help understand and moderate disagreements among raters in politically sensitive AI tasks.

Contribution

It introduces analytical methods using rater cohesion and quality metrics to analyze the influence of demographics on disagreement in vicarious annotation.

Findings

Rater cohesion varies with political and demographic backgrounds.

Demographic-aware quality metrics improve understanding of disagreement.

Vicarious annotation helps break down complex disagreements.

Abstract

Human feedback is essential for building human-centered AI systems across domains where disagreement is prevalent, such as AI safety, content moderation, or sentiment analysis. Many disagreements, particularly in politically charged settings, arise because raters have opposing values or beliefs. Vicarious annotation is a method for breaking down disagreement by asking raters how they think others would annotate the data. In this paper, we explore the use of vicarious annotation with analytical methods for moderating rater disagreement. We employ rater cohesion metrics to study the potential influence of political affiliations and demographic backgrounds on raters' perceptions of offense. Additionally, we utilize CrowdTruth's rater quality metrics, which consider the demographics of the raters, to score the raters and their annotations. We study how the rater quality metrics influence the in-group and cross-group rater cohesion across the personal and vicarious levels.