TY - JOUR AU - Jiang, Shan AU - Robertson, Ronald E. AU - Wilson, Christo PY - 2020/04/03 Y2 - 2024/03/28 TI - Reasoning about Political Bias in Content Moderation JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 09 SE - Sister Conference Track DO - 10.1609/aaai.v34i09.7117 UR - https://ojs.aaai.org/index.php/AAAI/article/view/7117 SP - 13669-13672 AB - <p>Content moderation, the AI-human hybrid process of removing (toxic) content from social media to promote community health, has attracted increasing attention from lawmakers due to allegations of political bias. Hitherto, this allegation has been made based on anecdotes rather than logical reasoning and empirical evidence, which motivates us to audit its validity. In this paper, we first introduce two formal criteria to measure bias (i.e., independence and separation) and their contextual meanings in content moderation, and then use YouTube as a lens to investigate if the political leaning of a video plays a role in the moderation decision for its associated comments. Our results show that when justifiable target variables (e.g., hate speech and extremeness) are controlled with propensity scoring, the likelihood of comment moderation is equal across left- and right-leaning videos.</p> ER -