The aim of this paper is to build models of governance for generative foundation models that enable broadly shared benefit. Our initial hypothesis is that data is a high-value lever of governance. Many of these models are trained on publicly available data and use public infrastructure, but 1) may d
AI and the knowledge commons
Funding the Commons | Public Goods Funding
We convene researchers, builders, and institutions to develop the funding mechanisms, governance systems, and coordination tools that public goods need in an age of AI.
AI Commons - One Project
What if we could redefine AI? What if we could shift its development from a capitalist model to a more disruptive, inclusive, and decentralized one?
A Roadmap to Democratic AI — The Collective Intelligence Project
Our roadmap outlines concrete steps that can be taken in 2024 to build a more democratic AI ecosystem that is adaptive, accountable, processes decentralized information, provides public goods, and safeguards human wellbeing. We describe what the ecosystem could build, research, advocate for, and fun
A Polycentric Governance Lens on Data Infrastructures
Funding policies for data infrastructure promote open data sharing to drive positive social impact. However, concerns regarding the long-term management of data within and across distributed infrastructures can hinder data sharing. We draw upon the concept of polycentric governance to demonstrate how collaborative practices of data curation, in preparing and maintaining data for (future) sharing, provide a solid foundation for understanding data governance within data infrastructures. Based on a qualitative case study of a distributed ecological network, we investigate the conditions under which data are managed as a shared resource by local actors to ensure the long-term (re)usability of data. We contribute to CSCW by conceptualising data curation as a complex form of governance practice with multiple centres of decision-making, each of which operates with some degree of autonomy in data infrastructures. A polycentric governance lens on data infrastructures advances the CSCW conception of data curation as a collective governance practice that can cultivate a data democracy culture within and across organisations, empower individuals to be accountable for their data, and foster a mindset shift toward decentralised data governance.
Can data collectives help strengthen vulnerable cultures in the face of AI?
"Data collectives and cooperatives, which let creators control the collection and distribution of their data, are emerging as preferred alternatives to big tech companies."
Democratizing AI Companies — The Collective Intelligence Project
As AI reshapes society, the companies behind these technologies face a critical challenge: how to build corporate governance structures that prioritize public benefit over profit maximization;.
From the Platform Society to the AI Society: Towards Critical Studies of Generative AI
The era of AI has begun. Generative AI is rapidly reshaping knowledge production, culture, and political authority, giving rise to an emerging AI society. Yet this transformation did not emerge ex nihilo. This paper argues that the AI society can only be understood in relation to the platform society from which it arises. Tracing the transition from platforms to AI, we identify interlinked economic, epistemic, and political shifts. Economically, AI emerges within platform-based rentier capitalism but reconfigures the monopoly mechanisms on which its accumulation depends. Epistemically, LLMs mark a shift from predictive to generative epistemics, entangling theory formation and knowledge production with private research-as-a-service infrastructures. Politically, governance shifts from data politics to alignment politics: from shaping visibility to shaping what can be said, thought, and imagined. Together, these transformations signal a qualitative shift in mediation—from governing interaction to governing cognition itself—and call for a Critical AI Studies.
The Ecosystem
Create collective knowledge by crowdsourcing and adapting governance innovations.
Collective Governance Directory
A collection of links to publicly available governance documents, published by the collectives that use them.
Who Pays for the Commons?
Three and a half years after the emergence of generative AI as a new technology paradigm, there is broad agreement that AI companies have extracted enorm...
Public Intelligence
Imagine 50 years from now a Public Intelligence that was a distributed, open-source, non-commercial artificial intelligence, operated like the internet, and available to the whole world. This public AI would be a federated system, not owned by any one entity, … Continue reading →
Open Source AI Policy Landscape — RedMonk Analysis
How foundations and projects are responding to AI-generated contributions. This analysis surveys 88 major organizations.
Data Streaming for AI: From Extractive Training to Sovereign Infrastructure DWeb Camp 2026
AI systems are consuming the world's content without compensating its creators. This session explores data streaming as a new paradigm — where content flows to AI in real time, with built-in rights management, usage tracking, and fair compensation — and asks what it would take to make this infrastructure decentralized, sovereign, and governed by the communities it serves.