Abstract This article examines the transformative impact of large language models (LLMs) on online content moderation, revealing a critical gap between platforms’ rule-based policies and their AI-driven enforcement mechanisms. Using Facebook’s hate speech moderation policies and practices as a case study, we identify a paradox: while content policies are increasingly rule-oriented, AI-driven enforcement seems to operate in a standard-like manner. This disconnect creates transparency, consistency and accountability challenges relating to the delineation of online freedom of expression that are not addressed in the literature, and require attention and mitigation. In this specific context, we introduce the concept of ‘rules by the millions’ to describe how AI systems actually operate through generating vast networks of micro-rules that evade traditional regulatory oversight. This phenomenon disrupts the conventional rules-versus-standards framework used in legal theory, raising urgent questions about the adequacy of current AI governance mechanisms. Indeed, the rapid adoption of LLMs in content moderation has outpaced the human capacity to monitor them, creating a pressing need for adaptive frameworks capable of managing the evolving capacities of AI.
Keydar et al. (Sun,) studied this question.