Article by Aline Blankertz & Svea Windwehr: “Governments, businesses and civil society representatives, among others, call for “alternatives” to compete with and possibly replace big tech platforms. These alternatives are usually characterized by different governance approaches like being not-for-profit, open, free, decentralized and/or community-based. We find that strengthening alternative governance models needs to account for the dynamic effects of operating in a digital ecosystem shaped by ad-driven platforms. Specifically, we explore in this article: 1) how interoperability between the microblogging platforms Threads (by Meta) and Mastodon (a not-for-profit service running on a federated open-source protocol) may foster competition, but also create a risk of converging governance in terms of e.g. content moderation and privacy practices; 2) how openness of the online encyclopedia Wikipedia allows Google Search to appropriate most of the value created by their vertical interaction and how the Wikimedia Foundation seeks to reduce that imbalance; 3) which types of interventions might be suitable to support alternatives without forcing them to emulate big tech governance, including asymmetric interoperability, digital taxes and regulatory restraints on commercial platforms…(More)”.
The Dangers of AI Nationalism and Beggar-Thy-Neighbour Policies
Paper by Susan Aaronson: “As they attempt to nurture and govern AI, some nations are acting in ways that – with or without direct intent – discriminate among foreign market actors. For example, some governments are excluding foreign firms from access to incentives for high-speed computing, or requiring local content in the AI supply chain, or adopting export controls for the advanced chips that power many types of AI. If policy makers in country X can limit access to the building blocks of AI – whether funds, data or high-speed computing power – it might slow down or limit the AI prowess of its competitors in country Y and/or Z. At the same time, however, such policies could violate international trade norms of non-discrimination. Moreover, if policy makers can shape regulations in ways that benefit local AI competitors, they may also impede the competitiveness of other nations’ AI developers. Such regulatory policies could be discriminatory and breach international trade rules as well as long-standing rules about how nations and firms compete – which, over time, could reduce trust among nations. In this article, the author attempts to illuminate AI nationalism and its consequences by answering four questions:
– What are nations doing to nurture AI capacity within their borders?
– Are some of these actions trade distorting?
– Are some nations adopting twenty-first century beggar thy neighbour policies?
– What are the implications of such trade-distorting actions?
The author finds that AI nationalist policies appear to help countries with the largest and most established technology firms across multiple levels of the AI value chain. Hence, policy makers’ efforts to dominate these sectors, as example through large investment sums or beggar thy neighbour policies are not a good way to build trust…(More)”.
Balancing Data Sharing and Privacy to Enhance Integrity and Trust in Government Programs
Paper by National Academy of Public Administration: “Improper payments and fraud cost the federal government hundreds of billions of dollars each year, wasting taxpayer money and eroding public trust. At the same time, agencies are increasingly expected to do more with less. Finding better ways to share data, without compromising privacy, is critical for ensuring program integrity in a resource-constrained environment.
Key Takeaways
- Data sharing strengthens program integrity and fraud prevention. Agencies and oversight bodies like GAO and OIGs have uncovered large-scale fraud by using shared data.
- Opportunities exist to streamline and expedite the compliance processes required by privacy laws and reduce systemic barriers to sharing data across federal agencies.
- Targeted reforms can address these barriers while protecting privacy:
- OMB could issue guidance to authorize fraud prevention as a routine use in System of Records Notices.
- Congress could enact special authorities or exemptions for data sharing that supports program integrity and fraud prevention.
- A centralized data platform could help to drive cultural change and support secure, responsible data sharing…(More)”
AI Agents in Global Governance: Digital Representation for Unheard Voices
Book by Eduardo Albrecht: “Governments now routinely use AI-based software to gather information about citizens and determine the level of privacy a person can enjoy, how far they can travel, what public benefits they may receive, and what they can and cannot say publicly. What input do citizens have in how these machines think?
In Political Automation, Eduardo Albrecht explores this question in various domains, including policing, national security, and international peacekeeping. Drawing upon interviews with rights activists, Albrecht examines popular attempts to interact with this novel form of algorithmic governance so far. He then proposes the idea of a Third House, a virtual chamber that legislates exclusively on AI in government decision-making and is based on principles of direct democracy, unlike existing upper and lower houses that are representative. Digital citizens, AI powered replicas of ourselves, would act as our personal emissaries to this Third House. An in-depth look at how political automation impacts the lives of citizens, this book addresses the challenges at the heart of automation in public policy decision-making and offers a way forward…(More)”.
Charting the AI for Good Landscape – A New Look
Article by Perry Hewitt and Jake Porway: “More than 50% of nonprofits report that their organization uses generative AI in day-to-day operations. We’ve also seen an explosion of AI tools and investments. 10% of all the AI companies that exist in the US were founded in 2022, and that number has likely grown in subsequent years. With investors funneling over $300B into AI and machine learning startups, it’s unlikely this trend will reverse any time soon.
Not surprisingly, the conversation about Artificial Intelligence (AI) is now everywhere, spanning from commercial uses such as virtual assistants and consumer AI to public goods, like AI-driven drug discovery and chatbots for education. The dizzying amount of new AI programs and initiatives – over 5000 new tools listed in 2023 on AI directories like TheresAnAI alone – can make the AI landscape challenging to navigate in general, much less for social impact. Luckily, four years ago, we surveyed the Data and AI for Good landscape and mapped out distinct families of initiatives based on their core goals. Today, we are revisiting that landscape to help folks get a handle on the AI for Good landscape today and to reflect on how the field has expanded, diversified, and matured…(More)”.
Data Commons: The Missing Infrastructure for Public Interest Artificial Intelligence
Article by Stefaan Verhulst, Burton Davis and Andrew Schroeder: “Artificial intelligence is celebrated as the defining technology of our time. From ChatGPT to Copilot and beyond, generative AI systems are reshaping how we work, learn, and govern. But behind the headline-grabbing breakthroughs lies a fundamental problem: The data these systems depend on to produce useful results that serve the public interest is increasingly out of reach.
Without access to diverse, high-quality datasets, AI models risk reinforcing bias, deepening inequality, and returning less accurate, more imprecise results. Yet, access to data remains fragmented, siloed, and increasingly enclosed. What was once open—government records, scientific research, public media—is now locked away by proprietary terms, outdated policies, or simple neglect. We are entering a data winter just as AI’s influence over public life is heating up.
This isn’t just a technical glitch. It’s a structural failure. What we urgently need is new infrastructure: data commons.
A data commons is a shared pool of data resources—responsibly governed, managed using participatory approaches, and made available for reuse in the public interest. Done correctly, commons can ensure that communities and other networks have a say in how their data is used, that public interest organizations can access the data they need, and that the benefits of AI can be applied to meet societal challenges.
Commons offer a practical response to the paradox of data scarcity amid abundance. By pooling datasets across organizations—governments, universities, libraries, and more—they match data supply with real-world demand, making it easier to build AI that responds to public needs.
We’re already seeing early signs of what this future might look like. Projects like Common Corpus, MLCommons, and Harvard’s Institutional Data Initiative show how diverse institutions can collaborate to make data both accessible and accountable. These initiatives emphasize open standards, participatory governance, and responsible reuse. They challenge the idea that data must be either locked up or left unprotected, offering a third way rooted in shared value and public purpose.
But the pace of progress isn’t matching the urgency of the moment. While policymakers debate AI regulation, they often ignore the infrastructure that makes public interest applications possible in the first place. Without better access to high-quality, responsibly governed data, AI for the common good will remain more aspiration than reality.
That’s why we’re launching The New Commons Challenge—a call to action for universities, libraries, civil society, and technologists to build data ecosystems that fuel public-interest AI…(More)”.
Entering the Vortex
Essay by Nils Gilman: “A strange and unsettling weather pattern is forming over the landscape of scholarly research. For decades, the climate of academic inquiry was shaped by a prevailing high-pressure system, a consensus grounded in the vision articulated by Vannevar Bush in “Science: The Endless Frontier” (1945). That era was characterized by robust federal investment, a faith in the university as the engine of basic research, and a compact that traded public funding for scientific autonomy and the promise of long-term societal benefit. It was a climate conducive to the slow, deliberate, and often unpredictable growth of knowledge, nurtured by a diverse ecosystem of human researchers — the vital “seed stock” of intellectual discovery.
But that high-pressure system is collapsing. A brutal, unyielding cold front of academic defunding has swept across the nation, a consequence of shifting political priorities, populist resentment, and a calculated assault on the university as an institution perceived as hostile to certain political agendas. This is not merely a belt-tightening exercise; it is, for all intents and purposes, the dismantling of Vannevar Bush’s Compact, the end of the era of “big government”-funded Wissenschaft. Funding streams for basic research are dwindling, grant applications face increasingly long odds, and the financial precarity of academic careers deters the brightest minds. The human capital necessary for sustained, fundamental inquiry is beginning to wither.
Simultaneously, a warm, moisture-laden airmass is rapidly advancing: the astonishing rise of AI-based research tools. Powered by vast datasets and sophisticated algorithms, these tools promise to revolutionize every stage of the research process – from literature review and data analysis to hypothesis generation and the drafting of scholarly texts. As a recent New Yorker piece on AI and the humanities suggests, these AI engines can already generate deep research and coherent texts on virtually any subject, seemingly within moments. They offer the prospect of unprecedented efficiency, speed, and scale in the production of scholarly output.
The collision of these two epochal weather systems — the brutal cold front of academic defunding and the warm, expansive airmass of AI-based research tools — is creating an atmospheric instability unlike anything the world of scholarship has ever witnessed. Along the front where these forces meet, a series of powerful and unpredictable tornados are beginning to touch down, reshaping the terrain of knowledge production in real-time…(More)”.
Real-time prices, real results: comparing crowdsourcing, AI, and traditional data collection
Article by Julius Adewopo, Bo Andree, Zacharey Carmichael, Steve Penson, Kamwoo Lee: “Timely, high-quality food price data is essential for shock responsive decision-making. However, in many low- and middle-income countries, such data is often delayed, limited in geographic coverage, or unavailable due to operational constraints. Traditional price monitoring, which relies on structured surveys conducted by trained enumerators, is often constrained by challenges related to cost, frequency, and reach.
To help overcome these limitations, the World Bank launched the Real-Time Prices (RTP) data platform. This effort provides monthly price data using a machine learning framework. The models combine survey results with predictions derived from observations in nearby markets and related commodities. This approach helps fill gaps in local price data across a basket of goods, enabling real-time monitoring of inflation dynamics even when survey data is incomplete or irregular.
In parallel, new approaches—such as citizen-submitted (crowdsourced) data—are being explored to complement conventional data collection methods. These crowdsourced data were recently published in a Nature Scientific Data paper. While the adoption of these innovations is accelerating, maintaining trust requires rigorous validation.
A newly published study in PLOS compares the two emerging methods with the traditional, enumerator-led gold standard, providing new evidence that both crowdsourced and AI-imputed prices can serve as credible, timely alternatives to traditional ground-truth data collection—especially in contexts where conventional methods face limitations…(More)”.
These Startups Are Building Advanced AI Models Without Data Centers
Article by Will Knight: “Researchers have trained a new kind of large language model (LLM) using GPUs dotted across the world and fed private as well as public data—a move that suggests that the dominant way of building artificial intelligence could be disrupted.
Article by Will Knight: “Flower AI and Vana, two startups pursuing unconventional approaches to building AI, worked together to create the new model, called Collective-1.
Flower created techniques that allow training to be spread across hundreds of computers connected over the internet. The company’s technology is already used by some firms to train AI models without needing to pool compute resources or data. Vana provided sources of data including private messages from X, Reddit, and Telegram.
Collective-1 is small by modern standards, with 7 billion parameters—values that combine to give the model its abilities—compared to hundreds of billions for today’s most advanced models, such as those that power programs like ChatGPT, Claude, and Gemini.
Nic Lane, a computer scientist at the University of Cambridge and cofounder of Flower AI, says that the distributed approach promises to scale far beyond the size of Collective-1. Lane adds that Flower AI is partway through training a model with 30 billion parameters using conventional data, and plans to train another model with 100 billion parameters—close to the size offered by industry leaders—later this year. “It could really change the way everyone thinks about AI, so we’re chasing this pretty hard,” Lane says. He says the startup is also incorporating images and audio into training to create multimodal models.
Distributed model-building could also unsettle the power dynamics that have shaped the AI industry…(More)”
Digital Public Infrastructure Could Make a Better Internet
Essay by Akash Kapur: “…The advent of AI has intensified geopolitical rivalries, and with them the risks of fragmentation, exclusion, and hyper-concentration that are already so prevalent. The prospects of a “Splinternet” have never appeared more real. The old dream of a global digital commons seems increasingly quaint; we are living amid what Yanis Varoufakis, the former Greek finance minister, calls “technofeudalism.”
DPI suggests it doesn’t have to be this way. The approach’s emphasis on loosening chokeholds, fostering collaboration, and reclaiming space from monopolies represents an effort to recuperate some of the internet’s original promise. At its most aspirational, DPI offers the potential for a new digital social contract: a rebalancing of public and private interests, a reorientation of the network so that it advances broad social goals even while fostering entrepreneurship and innovation. How fitting it would be if this new model were to emerge not from the entrenched powers that have so long guided the network, but from a handful of nations long confined to the periphery—now determined to take their seats at the table of global technology…(More)”.