Generative AI regulation can learn from social media regulation
Ruth Elisabeth Appel

TL;DR
This paper suggests that lessons from social media regulation can inform effective policies for regulating generative AI, emphasizing transparency, oversight, and global perspectives to prevent mistakes.
Contribution
It draws parallels between social media and generative AI regulation, offering specific policy recommendations based on social media regulatory evolution.
Findings
AI companies face bias allegations similar to social media platforms.
Regulatory strategies like transparency and oversight can be adapted for AI.
Global and research-focused approaches are crucial for effective AI regulation.
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…
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Taxonomy
TopicsEthics and Social Impacts of AI
