Poster
in
Workshop: Regulatable ML: Towards Bridging the Gaps between Machine Learning Research and Regulations
An Alternative to Regulation: The Case for Public AI
Nicholas Vincent · David Bau · Sarah Schwettmann · Joshua Tan
Abstract:
Can governments build AI? In this paper, we describe an ongoing effort to develop "public AI"—publicly accessible AI models funded, provisioned, and governed by governments or other public bodies. Public AI presents both an alternative and a complement to standard regulatory approaches to AI, but it also suggests new technical and policy challenges. We present a roadmap for how the ML research community can help shape this initiative and support its implementation, and how public AI can complement other responsible AI initiatives.
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