Vaccine Regulation Model Proposed for Frontier AI Governance, Citing Harvard Scholars Carpenter and Ezell
A recent LinkedIn article discusses the application of vaccine regulation models to the governance of frontier Artificial Intelligence (AI), drawing on a 2024 study by Harvard scholars Daniel Carpenter and Carson Ezell. The article argues for a 'middle setting' in AI regulation, advocating for a well-financed testing regime rather than an outright ban or unchecked development. This approach mirrors the phased testing and approval processes used for new medicines, which involve Phase I for safety, Phase II for efficacy, and Phase III for large-scale confirmation, followed by post-market surveillance. The author suggests that current AI development already has rough analogues to these phases, such as red-teaming for safety and proposals for AI incident-reporting databases. The piece emphasizes that the debate should focus on who verifies testing, on what timeline, and with what authority to halt development, rather than a binary choice between pausing or accelerating AI.
