Public AI Papers

On October 2, 2024, the Economic Security Project, Mozilla, and Vanderbilt Policy Accelerator hosted tech and economic policy experts to explore a path forward for Public AI in the United States. This page is the start of a list of relevant papers on the topic; this list is not intended to be exhaustive.

Papers from the hosts of the event:

• Public AI: Making AI Work For Everyone, By Everyone. Nik Marda, Jasmine Sun, Mark Surman. This paper makes the case for promoting public goods, public orientation, and public use throughout every step of AI development and deployment.
• The National Security Case for Public AI. Ganesh Sitaraman, Alex Pascal. This paper contends that to facilitate an AI future that supports American national security, the U.S. should embrace the public sector’s role in building, developing, and governing AI.
• Creating a Public Cloud through the Defense Production Act. Joel Dodge. To create its own public cloud infrastructure, the federal government would need to obtain GPUs. This paper outlines how the Defense Production Act can be used to acquire and pay for GPUs.
• Building a New Political Economy for AI. Economic Security Project. This paper provides a readout of a workshop in April 2024 that brought together 35 experts on AI and political economy to answer the question: “How can we govern tech and AI to deliver on the promise of broad-based prosperity?”
• Antimonopoly Tools for Regulating AI. Ganesh Sitaraman, Tejas Narechania. This paper offers technical background on the AI tech stack, and introduces the public option as a tool to combat concentrated private power throughout its layers.
• How States Can Keep Big Tech from Dominating AI. Natalie Foster, Ganesh Sitaraman. This op-ed surveys what some states have already done to advance Public AI.
• AI Is Too Important to Leave to Google and Facebook Alone. Ben Gansky, Michael Martin, and Ganesh Sitaraman. This op-ed argues for a public research consortium to take on useful AI projects that have no commercial prospects.

Papers from our friends who are also working to advance Public AI:

• Public AI: A New Approach to Public-Interest AI Investment. The Public AI Network. This paper argues for Public AI infrastructure with public access and public accountability that are permanently public.
• Defining Public AI. Serpentine Arts Technologies. This paper regards the notion of publicness as a spectrum on which the terms of public agency are negotiated, ranging from ‘thin’ to ‘thick’.
• The Role of Public Compute. Eleanor Shearer, Matt Davies, Mathew Lawrence. This article provides an overview of some of the challenges that will need to be addressed if the potential of public compute policies is to be realised.
• Build AI By The People, For The People. Bruce Schneier, Nathan E. Sanders. This op-ed calls for the public sector to invest in building public AI, given the risks of leaving AI entirely in the hands of Silicon Valley.
• AI Nationalism(s): Global Industrial Policy Approaches to AI. AI Now. This essay collection surveys the narratives and emergent industrial policies being proposed by governments with differing economic and geopolitical motivations.
• Dynamics of Corporate Governance Beyond Ownership in AI. Cecilia Rikap. Big Tech dominates investment in AI technology; this piece calls for a public strategy for regulation is crucial to challenge uneven distributions of economic power.
• Democratizing AI for the Public Good. Lea Gimpel, Daniel Brumund, Alek Tarkowski, Maximilian Gahntz, Urvashi Aneja, Vukosi Marivate, Anita Gurumurthy. This T20 policy brief considers how open source AI as digital public goods can be instrumental for making progress toward AI democratization.
• Take Back the Future: Democratising Power over AI. Eleanor Shearer. This piece outlines a new research program to study where other examples of democratizing the economy gives us useful conceptual frameworks that might apply to the development of AI.
• A ‘CERN for AI’ – what might an international AI research organization address. Elliot Jones. This piece akss whether a model similar to the CERN nuclear physics laboratory could work for AI governance.
• Rethinking data and rebalancing digital power. Valentina Pavel. This paper asks what a more ambitious vision for data use and regulation could look like, and offers a vision for a "BBC for data.
• Great British Cloud and BritGPT. Haydn Belfield. This piece lays out a plan for founding and investing in two new publicly owned companies, the Great British Cloud and BritGPT.

Is your favorite paper missing here? Send it to nik at mozillafoundation dot org.