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.
Published Date: 2020-06-02
Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print) ISBN 978-1-57735-835-0 (10 issue set)
Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2020, Association for the Advancement of Artificial Intelligence All Rights Reserved