In this submission, the Center for Data Innovation encourage U.S. policymakers to learn lessons from past debates about dual-use technologies, such as encryption, and refrain from imposing restrictions on foundation models with widely available model weights (i.e. “open models”) because such policies would not only be ultimately ineffective at addressing risk, but they would slow innovation, reduce competition, and decrease U.S. competitiveness. NTIA should use an evidence-based approach to addressing AI risks and avoid broad rules that would negatively impact the ability to develop open models. Moreover, U.S. policymakers should defend open AI models at the international level as part of its continued embrace of the global free flow of data.
Comments to NTIA on Dual Use Foundation Artificial Intelligence Models With Widely Available Model Weights
Daniel Castro
Daniel Castro is president of the Information Technology and Innovation Foundation (ITIF), the world’s top-ranked think tank for science and technology policy, where he leads the organization’s work shaping debates on critical issues at the intersection of technological innovation and public policy. He is a prolific writer and respected public speaker on issues ranging from Internet policy and digital governance to artificial intelligence and other emerging technologies. Castro also founded and is director of ITIF’s Center for Data Innovation, a leading voice on open data, artificial intelligence, and digital transformation. He has played a key role in advancing the OPEN Government Data Act and has long worked on the policy and economic impact of data-driven innovation. His work on AI spans a broad range of policy issues, including adoption and diffusion, privacy and security, intellectual property, deepfakes and information integrity, and the economic and competitive dynamics of AI, as well as global competition and governance—particularly in China and Europe.
