Generative AI regulation can learn from social media regulation

• URL: http://arxiv.org/abs/2412.11335v1
• Date: Sun, 15 Dec 2024 23:00:29 GMT
• Title: Generative AI regulation can learn from social media regulation
• Authors: Ruth Elisabeth Appel,
• Abstract summary: I argue that the debates on generative AI regulation can be informed by the debates and evidence on social media regulation. I compare and contrast the affordances of generative AI and social media to highlight their similarities and differences. I discuss specific policy recommendations based on the evolution of social media and their regulation.
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• Abstract: There is strong agreement that generative AI should be regulated, but strong disagreement on how to approach regulation. While some argue that AI regulation should mostly rely on extensions of existing laws, others argue that entirely new laws and regulations are needed to ensure that generative AI benefits society. In this paper, I argue that the debates on generative AI regulation can be informed by the debates and evidence on social media regulation. For example, AI companies have faced allegations of political bias regarding the images and text their models produce, similar to the allegations social media companies have faced regarding content ranking on their platforms. First, I compare and contrast the affordances of generative AI and social media to highlight their similarities and differences. Then, I discuss specific policy recommendations based on the evolution of social media and their regulation. These recommendations include investments in: efforts to counter bias and perceptions thereof (e.g., via transparency, researcher access, oversight boards, democratic input, research studies), specific areas of regulatory concern (e.g., youth wellbeing, election integrity) and trust and safety, computational social science research, and a more global perspective. Applying lessons learnt from social media regulation to generative AI regulation can save effort and time, and prevent avoidable mistakes.

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