2022/07/01 by Tarleton Gillespie · 1 voice · 12 citations
Computer Science · Social Sciences · #Ethics and Social Impacts of AI #Hate Speech and Cyberbullying Detection #Law, AI, and Intellectual Property
paper · doi:10.1177/20563051221117552
openalex publication_date 2022/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
Public debate about content moderation has overwhelmingly focused on removal: social media platforms deleting content and suspending users, or opting not to do so. However, removal is not the only available remedy. Reducing the visibility of problematic content is becoming a commonplace element of platform governance. Platforms use machine learning classifiers to identify content they judge misleading enough, risky enough, or offensive enough that, while it does not warrant removal according to the site guidelines, warrants demoting them in algorithmic rankings and recommendations. In this essay, I document this shift and explain how reduction works. I then raise questions about what it means to use recommendation as a means of content moderation.