Ideology-Based LLMs for Content Moderation
The study tested how giving Large Language Models (LLMs) specific 'personas'—representing different political ideologies—affects their content moderation decisions. Findings show that while overall accuracy remains stable, personas cause models to align more closely with their own ideology and create distinct biases when moderating hate speech, suggesting persona conditioning amplifies ideological leanings.