An Alternative to Regulation: The Case for Public AI

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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Cite

Text

Vincent et al. "An Alternative to Regulation: The Case for Public AI." NeurIPS 2023 Workshops: RegML, 2023.

Markdown

[Vincent et al. "An Alternative to Regulation: The Case for Public AI." NeurIPS 2023 Workshops: RegML, 2023.](https://mlanthology.org/neuripsw/2023/vincent2023neuripsw-alternative/)

BibTeX

@inproceedings{vincent2023neuripsw-alternative,
  title     = {{An Alternative to Regulation: The Case for Public AI}},
  author    = {Vincent, Nicholas and Bau, David and Schwettmann, Sarah and Tan, Joshua},
  booktitle = {NeurIPS 2023 Workshops: RegML},
  year      = {2023},
  url       = {https://mlanthology.org/neuripsw/2023/vincent2023neuripsw-alternative/}
}