Position: Generative AI Regulation Can Learn from Social Media Regulation

Ruth Elisabeth Appel

International Conference on Machine Learning 2025 · Oral

In this thought-provoking talk at ICML 2025, Ruth Elisabeth Appel, a postdoctoral fellow at Stanford University at the time of the research and now with Anthropic, presented a compelling argument for leveraging insights from social media regulation to inform the burgeoning field of generative AI governance. Titled "Generative AI Regulation Can Learn from Social Media Regulation," Appel's paper challenges the prevailing notion that the transformative nature of AI necessitates entirely novel legal frameworks, advocating instead for a pragmatic approach that builds upon existing regulatory precedents and lessons learned from previous technological paradigms. The core of her argument posits that despite their differences, generative AI and social media share fundamental characteristics, particularly concerning societal impact and content governance, that make a comparative analysis not only possible but essential.

AI review

A policy position paper that draws structural analogies between social media and generative AI regulation and extracts four governance recommendations. The argument is coherent and the framing is timely, but this is not a technical ML contribution — it is a piece of science policy writing, and it should be evaluated as such. The core analogy is reasonable but underdeveloped; the recommendations are sensible but not rigorously derived; and the claim that regulatory lessons are 'transferable' is asserted rather than demonstrated. For an ICML audience expecting either theoretical results or…