Automated Moderation Is Here to Stay—Accountability Must Keep Pace

This post is part 2 in a series about automated content moderation. Read the first post here. When whistleblower Frances Haugen leaked a set of documents from Meta in 2020, among the revelations was a jarring statistic: The company’s algorithms designed to detect terrorist content incorrectly deleted nonviolent Arabic-language content 77 percent of the time, while failing to detect hate speech under the company’s own policies in many instances. Meta’s own transparency report released later that year demonstrated similar findings. Five years later, researchers in the region report that overzealous moderation remains a problem, while paths to remedy have all but collapsed. Where these systems are faltering in Arabic, they’re positively failing in less-resourced languages. As a 2025 report from the Center for Democracy and Technology found, labeled datasets in certain languages and dialects such as Maghrebi Arabic and Kiswahili contain inconsistencies, bias, and inaccuracies due to the li