Commons suffer from neglect, free-riding, and a persistent deficit of care. Inspired by Shinto animism -- where every forest, river, and mountain has its own \emph{kami}, a spirit that inhabits and cares for that place -- we provoke: what if every commons had its own AI steward? Through a speculative design workshop where fifteen participants used Protocol Futuring, we surface both new opportunities and new dangers. Agentic AI offers the possibility of continuously supporting commons with programmable agency and care -- stewards that mediate family life as the most intimate commons, preserve collective knowledge, govern shared natural resources, and sustain community welfare. But when every commons has its own steward, second-order effects emerge: stewards contest stewards as overlapping commons collide; individuals caught between multiple stewards face new politics of care and constraint; the stewards themselves become commons requiring governance. This work opens \emph{agentive governance as commoning design material} -- a new design space for the agency, care ethics, and accountability of AI stewards of shared resources -- radically different from surveillance or optimization.
This study investigates Wikimedia Commons contributors' lived experiences with the Computer-Aided Tagging (CAT) tool, an AI-assisted image tagging system designed to improve Commons' discoverability, searchability, accessibility, and multilingual support. Using a qualitative analysis of 595 CAT-related community comments from 11 wiki pages and 16 in-depth interviews, we identify seven key issues that contributed to CAT's mixed reception and eventual deactivation. We also offer community-informed suggestions for improving the tool. We reflect on the implications for designing human-AI collaboration on Commons and for developing AI-assisted tools that support open knowledge work. This work contributes to HCI and CSCW research by extending the understanding of human-AI collaboration beyond Anglophone, text-centric, corporate platforms.
The rapid advancement of Generative AI (GenAI) relies heavily on the digital commons, a vast collection of free and open online content that is created, shared, and maintained by communities. However, this relationship is becoming increasingly strained due to financial burdens, decreased contributions, and misalignment between AI models and community norms. As we move deeper into the GenAI era, it is essential to examine the interdependent relationship between GenAI, the long-term sustainability of the digital commons, and the equity of current AI development practices. We highlight five critical questions that require urgent attention: 1. How can we prevent the digital commons from being threatened by undersupply as individuals cease contributing to the commons and turn to Generative AI for information? 2. How can we mitigate the risk of the open web closing due to restrictions on access to curb AI crawlers? 3. How can technical standards and legal frameworks be updated to reflect the evolving needs of organizations hosting common content? 4. What are the effects of increased synthetic content in open knowledge databases, and how can we ensure their integrity? 5. How can we accoun
Gen3 is an open-source data platform for building data commons. A data commons is a cloud-based data platform for managing, analyzing, and sharing data with a research community. Gen3 has been used to build over a dozen data commons that in aggregate contain over 28 PB of data and 64 million FAIR data objects. To set up a Gen3 data commons, you first define a data model. Gen3 then autogenerates 1) a data portal for searching and exploring data in the commons; 2) a data portal for submitting data to the commons; and 3) FAIR APIs for accessing the data programmatically. Gen3 is built over a small number of standards-based software services, which are designed to support current and future Gen3 components so that Gen3 can interoperate with other data platforms and data ecosystems.
The fragmentation of public data in Brazil, coupled with inconsistent standards and limited interoperability, hinders effective research, evidence-based policymaking and access to data-driven insights. To address these issues, we introduce Brazil Data Commons, a platform that unifies various Brazilian datasets under a common semantic framework, enabling the seamless discovery, integration and visualization of information from different domains. By adopting globally recognized ontologies and interoperable data standards, Brazil Data Commons aligns with the principles of the broader Data Commons ecosystem and places Brazilian data in a global context. Through user-friendly interfaces, straightforward query mechanisms and flexible data access options, the platform democratizes data use and enables researchers, policy makers, and the public to gain meaningful insights and make informed decisions. This paper illustrates how Brazil Data Commons transforms scattered datasets into an integrated and easily navigable resource that allows a deeper understanding of Brazil's complex social, economic and environmental landscape.
A data commons brings together (or co-locates) data with cloud computing infrastructure and commonly used software services, tools and applications for managing, analyzing and sharing data to create an interoperable resource for a research community. We introduce an architectural design principle for data commons called the narrow middle architecture that is broadly based upon the end-to-end argument in systems design. We also discuss important core services for data commons and the role of standards.
Computing is accompanied by both positive and negative commons throughout its lifecycle of creation, execution, and disposal. We examine two governance systems situated within this lifecycle -- global e-waste trade and the Linux kernel community -- to evaluate whether Elinor Ostrom's eight design principles for common-pool resource (CPR) governance extend to the management of negative common-pool resources (NCPRs). Unlike traditional CPRs where communities work to preserve a finite resource (i.e. clean water), NCPR governance seeks to collectively reduce a negative shared stock. In our two cases, e-waste governance aims to reduce the volume of mismanaged waste and illicit trade, while the Linux community aims to reduce the number of error-prone or malicious contributions that reach the main branch and, in turn, extend the life of existing hardware. Through qualitative analysis of primary sources from each domain, we find that the same eight principles by Ostrom that aid positive commons governance tend to appear in successful negative commons governance systems. We argue that future NCPR governance design should prioritize Ostrom's principles, particularly clearly defined boundarie
In this article, we argue that AI slop in software is creating a tragedy of the commons. Individual productivity gains from AI-generated content externalize costs onto reviewer capacity, codebase integrity, public knowledge resources, collaborative trust, and the talent pipeline. AI slop is cheap to generate and expensive to review, and the review layer is already thin. Commons problems are not solved by individual restraint. We outline concrete next steps for tool developers, team leads, and educators, grounded in Ostrom's design principles for enduring commons institutions.
This article describes the use of metadata and standards in the Social Impact Data Commons to expose official statisticians to an innovative project built on actionable and evaluable metadata, which produces a FAIR data system. We begin by introducing the concept of the Data Commons, focusing on its features, and presenting an overview of current implementations of the Data Commons. We then present the core metadata case study, demonstrating how smart metadata support the Data Commons. We also present evaluations of our core metadata, including its adherence to the FAIR guidelines. We conclude with a discussion on our future metadata and standards-related projects to support the Social Impact Data Commons.
Communities can sustainably manage shared resources (commons) through self-governance and cooperative norms, a central finding of Ostrom's theory of self-governance. However, real-world commons (e.g., fisheries, forests, and irrigation systems) are often governed under asymmetric power structures, where certain individuals or institutions possess disproportionate control over resource extraction and collective outcomes. As Large Language Models (LLMs) are increasingly explored as agents in synthetic governance simulations, understanding how LLM societies behave under asymmetric power structures is becoming increasingly important, yet existing evaluations largely ignore such asymmetries. We introduce Sovereignty over the Commons Simulation (SovSim), a generative multi-agent simulation framework that incorporates an agent with asymmetric power (boss or king) into a society of symmetric agents (workers or peasants), where all agents extract from a shared resource, collectively determining its sustainability over time. Across eleven state-of-the-art models, we find that introducing asymmetric power leads to severe breakdowns in cooperation and sustainability, with up to an 87.3% degrad
Self-optimizing behaviors can lead to outcomes where collective benefits are ultimately destroyed, a well-known phenomenon known as the ``tragedy of the commons". These scenarios are widely studied using game-theoretic approaches to analyze strategic agent decision-making. In this paper, we examine this phenomenon in a bi-level decision-making hierarchy, where low-level agents belong to multiple distinct populations, and high-level agents make decisions that impact the choices of the local populations they represent. We study strategic interactions in a context where the populations benefit from a common environmental resource that degrades with higher extractive efforts made by high-level agents. We characterize a unique symmetric Nash equilibrium in the high-level game, and investigate its consequences on the common resource. While the equilibrium resource level degrades as the number of populations grows large, there are instances where it does not become depleted. We identify such regions, as well as the regions where the resource does deplete.
In a recent dynamic model by Acemoglu, Kong and Ozdaglar (2026a) agentic AI can cause a self-reinforcing deterioration of humanity's common knowledge base in what they call knowledge collapse. The model is based on a natural complementarity between the cumulative general knowledge of humans and locally generated context-specific knowledge, and on a learning externality that means that we all contribute to the private signal and the thin public signal that feeds the collective stock. If agentic AI can substitute for the private signal but not rebuild the public signal, and when human effort is sufficiently elastic, we can reach a low-knowledge equilibrium. This paper offers a measured appraisal of the model. It assesses the model in terms of what the popular summaries say is common knowledge, the world literature against knowledge commons and model collapse, and the partial empirical evidence, such as a 25% decline in public knowledge sharing on Stack Overflow. It then presents five structural criticisms of the model: its fixed knowledge taxonomy, the assumption that AI cannot provide general knowledge, the unmeasured effort elasticity parameter, and the political futility of the mo
Hardin introduced the notorious concept of "tragedy of the commons". Worrying about the consequences of human overpopulation on the planet, he discussed "hard problems": problems with no technical solutions, that can only be addressed by way of an evolving morality. Hardin's tragedy of the commons predicts that the hard problem of human population growth directly implies a hard problem of overuse or pollution of the commons. This paper focuses on the knowledge commons. A technical proposal is presented, based on a JSON schema for structuring pieces of knowledge. This is used to show that even if the knowledge commons satisfies the necessary conditions of the tragedy of the commons, the ensuing problems are not necessarily hard. Some can be made trivial by relying on traditional principles implemented in a technical framework.
The governance of artificial intelligence is overwhelmingly theorized through two institutional frames. In the market frame, the data, models, and compute that constitute the AI stack are private goods exchanged under property and contract; in the state frame, a regulator imposes rules from above. A third possibility, the collective and self-organized stewardship of AI-relevant resources by the communities that produce and depend on them, remains comparatively under-theorized, even as it proliferates in practice through data trusts and cooperatives, federated learning consortia, public compute initiatives, open-weight model collaborations, and community data sovereignty regimes. This article argues that these arrangements form a coherent institutional family, which we call commons-governed artificial intelligence, and that the analytic vocabulary developed by Elinor Ostrom and her successors for common-pool and knowledge commons is the right backbone for classifying them. We contribute a two-dimensional taxonomy whose first axis is the resource layer of the AI stack held in common, distinguishing data, compute, models, knowledge and evaluation, and energy, and whose second axis is
Artificial intelligence is reshaping cognitive work, but Human Resource Development scholarship has treated this transformation as an organizational training challenge, leaving the collective regeneration of professional expertise unexamined. This conceptual paper introduces the Cognitive Commons framework, integrating commons theory, HRD scholarship, and distributed cognition to explain how rational AI adoption decisions can deplete the shared expertise pool professions require for renewal. The framework distinguishes Internalized Mastery (deep domain knowledge from sustained practice) from Distributed Mastery (orchestrating human-AI systems), and develops the Validation Tether: effective AI oversight depends on the expertise AI adoption may undermine. Early labor market and clinical evidence suggests possible disruption to expertise-regeneration pathways in highly AI-exposed sectors, though adoption is recent and the strongest signals come from leading sectors rather than all professions. Five factors determine occupational vulnerability, and governance arrangements may form across organizational, professional-association, and policy levels. The paper reframes expertise developme
Large language model development relies on large-scale training corpora, yet most contain data of unclear licensing status, limiting the development of truly open models. This problem is exacerbated for non-English languages, where openly licensed text remains critically scarce. We introduce the German Commons, the largest collection of openly licensed German text to date. It compiles data from 41 sources across seven domains, encompassing legal, scientific, cultural, political, news, economic, and web text. Through systematic sourcing from established data providers with verifiable licensing, it yields 154.56 billion tokens of high-quality text for language model training. Our processing pipeline implements comprehensive quality filtering, deduplication, and text formatting fixes, ensuring consistent quality across heterogeneous text sources. All domain subsets feature licenses of at least CC-BY-SA 4.0 or equivalent, ensuring legal compliance for model training and redistribution. The German Commons therefore addresses the critical gap in openly licensed German pretraining data, and enables the development of truly open German language models. We also release code for corpus const
Publicly available data from open sources (e.g., United States Census Bureau (Census), World Health Organization (WHO), Intergovernmental Panel on Climate Change (IPCC)) are vital resources for policy makers, students and researchers across different disciplines. Combining data from different sources requires the user to reconcile the differences in schemas, formats, assumptions, and more. This data wrangling is time consuming, tedious and needs to be repeated by every user of the data. Our goal with Data Commons (DC) is to help make public data accessible and useful to those who want to understand this data and use it to solve societal challenges and opportunities. We do the data processing and make the processed data widely available via standard schemas and Cloud APIs. Data Commons is a distributed network of sites that publish data in a common schema and interoperate using the Data Commons APIs. Data from different Data Commons can be joined easily. The aggregate of these Data Commons can be viewed as a single Knowledge Graph. This Knowledge Graph can then be searched over using Natural Language questions utilizing advances in Large Language Models. This paper describes the arc
The tragedy of the commons has traditionally been framed as a problem of resource overuse driven by self-interested exploitation. In contrast, growing empirical evidence shows that insufficient use or abandonment of natural resources, known as underuse, can also lead to ecological degradation and loss of ecosystem services. Despite its relevance, underuse has rarely been examined within evolutionary theories of resource use. Here, we develop a simple eco-evolutionary model that integrates both provisioning and non-provisioning ecosystem services to analyze the evolution of resource-use strategies. Using adaptive dynamics, we investigate how individual resource use evolves while altering resource abundance. The model shows that overuse and underuse arise naturally as alternative evolutionary outcomes of the same underlying process, alongside intermediate use and evolutionary branching. We derive analytical conditions for the existence, number, and stability of evolutionarily singular strategies, and show that the qualitative evolutionary fate is primarily determined by the shape of provisioning benefits. Only when provisioning benefits increase in a concave manner does evolutionary
The problems raised by anti-commons and bureaucracy have been linked since the study of Buchanan and Yoon (2000). Bureaucracy involves a multitude of agents that have deciding power. At the view of conflicting interests, the decision makers inertia or the inertia of the system itself, excessive administrative procedures or too many administrative circuits push for too late decisions, or for non-rational decisions in terms of value creation for economic agents. Property Rights Theory explains new concerns. Considering that an anti-commons problem arises when there are multiple rights to exclude, the problem of decision process in aquaculture projects makes sense at this level. However, little attention has been given to the setting where more than one person is assigned exclusion rights, which may be exercised. Anti-commons problem is analyzed in situations in which resources are inefficiently under-utilized rather than over-utilized as in the familiar commons setting. In this study, fisheries problems are studied and some ways to deal with the problem are presented.
Large Language Models (LLMs) are entering urban governance, yet their outputs are highly sensitive to prompts that carry value judgments. We propose Prompt Commons - a versioned, community-maintained repository of prompts with governance metadata, licensing, and moderation - to steer model behaviour toward pluralism. Using a Montreal dataset (443 human prompts; 3,317 after augmentation), we pilot three governance states (open, curated, veto-enabled). On a contested policy benchmark, a single-author prompt yields 24 percent neutral outcomes; commons-governed prompts raise neutrality to 48-52 percent while retaining decisiveness where appropriate. In a synthetic incident log, a veto-enabled regime reduces time-to-remediation for harmful outputs from 30.5 +/- 8.9 hours (open) to 5.6 +/- 1.5 hours. We outline licensing (CC BY/BY-SA for prompts with optional OpenRAIL-style restrictions for artefacts), auditable moderation, and safeguards against dominance capture. Prompt governance offers a practical lever for cities to align AI with local values and accountability.