Annotator Bias Transferred into the Dataset
標註者偏誤傳遞至資料集
Design & Development
Risk Description
When the annotation team for a content moderation model is homogeneous and misjudges expressions specific to a particular cultural context; due to unmitigated control gaps, after launch, the system flags large volumes of ordinary expression from that community as violations, provoking backlash and forcing the organization to re-annotate and retrain, triggering compliance exposure and operational reputational costs.
Framework Mappings
EU AI ActArt.10
NIST AI 600-1Harmful Bias and Homogenization
NIST AI RMFMEASURE 2.11
ISO/IEC TR 24027
ISO/IEC 5338設計與開發
MIT AI Risk RepositoryDomain 1
Risk Treatment & Implementation Guidance
Build labeling teams with diverse backgrounds covering the cultural contexts of the main user base; Standardize labeling guidelines with cultural-context precedents to reduce individual variance; Test annotation consistency on cross-cultural samples, revising guidelines and relabeling on divergence