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.
@article{arxiv.2311.11350,
title = {An Alternative to Regulation: The Case for Public AI},
author = {Nicholas Vincent and David Bau and Sarah Schwettmann and Joshua Tan},
journal= {arXiv preprint arXiv:2311.11350},
year = {2023}
}
Comments
To be presented at Regulatable ML @ NeurIPS2023 workshop