OECD Report: “To achieve ambitious climate targets under the Paris Agreement, countries need more than political will – they need effective governance. This report examines how a mission-oriented approach can transform climate action. Analysing 15 countries’ climate council assessments, the report reveals that, while many nations are incorporating elements of mission governance, significant gaps remain. It highlights promising examples of whole-of-government approaches, while identifying key challenges, such as limited societal engagement, weak co-ordination, and a lack of focus on experimentation and ecosystem mobilisation. The report argues that national climate commitments effectively function as overarching missions, and thus, can greatly benefit from applying mission governance principles. It recommends integrating missions into climate mitigation efforts, applying these principles to policy design and implementation, and deploying targeted missions to address specific climate challenges. By embracing a holistic, mission-driven strategy, countries can enhance their climate action and achieve their ambitious targets…(More)”.
How crawlers impact the operations of the Wikimedia projects
Article by the Wikimedia Foundation: “Since the beginning of 2024, the demand for the content created by the Wikimedia volunteer community – especially for the 144 million images, videos, and other files on Wikimedia Commons – has grown significantly. In this post, we’ll discuss the reasons for this trend and its impact.
The Wikimedia projects are the largest collection of open knowledge in the world. Our sites are an invaluable destination for humans searching for information, and for all kinds of businesses that access our content automatically as a core input to their products. Most notably, the content has been a critical component of search engine results, which in turn has brought users back to our sites. But with the rise of AI, the dynamic is changing: We are observing a significant increase in request volume, with most of this traffic being driven by scraping bots collecting training data for large language models (LLMs) and other use cases. Automated requests for our content have grown exponentially, alongside the broader technology economy, via mechanisms including scraping, APIs, and bulk downloads. This expansion happened largely without sufficient attribution, which is key to drive new users to participate in the movement, and is causing a significant load on the underlying infrastructure that keeps our sites available for everyone.
When Jimmy Carter died in December 2024, his page on English Wikipedia saw more than 2.8 million views over the course of a day. This was relatively high, but manageable. At the same time, quite a few users played a 1.5 hour long video of Carter’s 1980 presidential debate with Ronald Reagan. This caused a surge in the network traffic, doubling its normal rate. As a consequence, for about one hour a small number of Wikimedia’s connections to the Internet filled up entirely, causing slow page load times for some users. The sudden traffic surge alerted our Site Reliability team, who were swiftly able to address this by changing the paths our internet connections go through to reduce the congestion. But still, this should not have caused any issues, as the Foundation is well equipped to handle high traffic spikes during exceptional events. So what happened?…
Since January 2024, we have seen the bandwidth used for downloading multimedia content grow by 50%. This increase is not coming from human readers, but largely from automated programs that scrape the Wikimedia Commons image catalog of openly licensed images to feed images to AI models. Our infrastructure is built to sustain sudden traffic spikes from humans during high-interest events, but the amount of traffic generated by scraper bots is unprecedented and presents growing risks and costs…(More)”.
Prosocial Media
Paper by Glen Weyl et al: “Social media empower distributed content creation by algorithmically harnessing “the social fabric” (explicit and implicit signals of association) to serve this content. While this overcomes the bottlenecks and biases of traditional gatekeepers, many believe it has unsustainably eroded the very social fabric it depends on by maximizing engagement for advertising revenue. This paper participates in open and ongoing considerations to translate social and political values and conventions, specifically social cohesion, into platform design. We propose an alternative platform model that includes the social fabric an explicit output as well as input. Citizens are members of communities defined by explicit affiliation or clusters of shared attitudes. Both have internal divisions, as citizens are members of intersecting communities, which are themselves internally diverse. Each is understood to value content that bridge (viz. achieve consensus across) and balance (viz. represent fairly) this internal diversity, consistent with the principles of the Hutchins Commission (1947). Content is labeled with social provenance, indicating for which community or citizen it is bridging or balancing. Subscription payments allow citizens and communities to increase the algorithmic weight on the content they value in the content serving algorithm. Advertisers may, with consent of citizen or community counterparties, target them in exchange for payment or increase in that party’s algorithmic weight. Underserved and emerging communities and citizens are optimally subsidized/supported to develop into paying participants. Content creators and communities that curate content are rewarded for their contributions with algorithmic weight and/or revenue. We discuss applications to productivity (e.g. LinkedIn), political (e.g. X), and cultural (e.g. TikTok) platforms…(More)”.
AI, Innovation and the Public Good: A New Policy Playbook
Paper by Burcu Kilic: “When Chinese start-up DeepSeek released R1 in January 2025, the groundbreaking open-source artificial intelligence (AI) model rocked the tech industry as a more cost-effective alternative to models running on more advanced chips. The launch coincided with industrial policy gaining popularity as a strategic tool for governments aiming to build AI capacity and competitiveness. Once dismissed under neoliberal economic frameworks, industrial policy is making a strong comeback with more governments worldwide embracing it to build digital public infrastructure and foster local AI ecosystems. This paper examines how the national innovation system framework can guide AI industrial policy to foster innovation and reduce reliance on dominant tech companies…(More)”.
DOGE comes for the data wonks
The Economist: “For nearly three decades the federal government has painstakingly surveyed tens of thousands of Americans each year about their health. Door-knockers collect data on the financial toll of chronic conditions like obesity and asthma, and probe the exact doses of medications sufferers take. The result, known as the Medical Expenditure Panel Survey (MEPS), is the single most comprehensive, nationally representative portrait of American health care, a balkanised and unwieldy $5trn industry that accounts for some 17% of GDP.
MEPS is part of a largely hidden infrastructure of government statistics collection now in the crosshairs of the Department of Government Efficiency (DOGE). In mid-March officials at a unit of the Department of Health and Human Services (HHS) that runs the survey told employees that DOGE had slated them for an 80-90% reduction in staff and that this would “not be a negotiation”. Since then scores of researchers have taken voluntary buyouts. Those left behind worry about the integrity of MEPS. “Very unclear whether or how we can put on MEPS” with roughly half of the staff leaving, one said. On March 27th, the health secretary, Robert F. Kennedy junior, announced an overall reduction of 10,000 personnel at the department, in addition to those who took buyouts.
There are scores of underpublicised government surveys like MEPS that document trends in everything from house prices to the amount of lead in people’s blood. Many provide standard-setting datasets and insights into the world’s largest economy that the private sector has no incentive to replicate.
Even so, America’s system of statistics research is overly analogue and needs modernising. “Using surveys as the main source of information is just not working” because it is too slow and suffers from declining rates of participation, says Julia Lane, an economist at New York University. In a world where the economy shifts by the day, the lags in traditional surveys—whose results can take weeks or even years to refine and publish—are unsatisfactory. One practical reform DOGE might encourage is better integration of administrative data such as tax records and social-security filings which often capture the entire population and are collected as a matter of course.
As in so many other areas, however, DOGE’s sledgehammer is more likely to cause harm than to achieve improvements. And for all its clunkiness, America’s current system manages a spectacular feat. From Inuits in remote corners of Alaska to Spanish-speakers in the Bronx, it measures the country and its inhabitants remarkably well, given that the population is highly diverse and spread out over 4m square miles. Each month surveys from the federal government reach about 1.5m people, a number roughly equivalent to the population of Hawaii or West Virginia…(More)”.
Public Governance and Emerging Technologies
Book edited by Jurgen Goossens, Esther Keymolen, and Antonia Stanojević: “This open access book focuses on public governance’s increasing reliance on emerging digital technologies. ‘Disruptive’ or ‘emerging’ digital technologies, such as artificial intelligence and blockchain, are often portrayed as highly promising, with the potential to transform established societal, economic, or governmental practices. Unsurprisingly, public actors are therefore increasingly experimenting with the application of these emerging digital technologies in public governance.
The first part of the book shows how automatization via algorithmic systems, the networked nature of distributed technologies such as blockchain, and data-driven use of AI in public governance can promote hyper-connectivity and hyper-complexity. This trend and the associated concerns have drawn societal, political, and scholarly attention to regulatory compliance considering the current and potential future uses of emerging technologies. Accordingly, the second part of the book focuses on regulatory compliance and regulatory solutions. It explores the compatibility of technology with existing regulations, existing legal tools that could be innovatively applied for the successful regulation of emerging technologies, and approaches to updating existing legislation or creating new legislation for the regulation of emerging technologies. While socio-ethical considerations on upholding public values in a digital world are at the heart of all chapters, the third part specifically focuses on public values and trust. It advances a conceptual, normative discussion, putting the spotlight on trust and other fundamental public values that should be safeguarded…(More)”
How governments can move beyond bureaucracy
Interview with Jorrit de Jong: “..Bureaucracy is not so much a system of rules, it is a system of values. It is an organizational form that governs how work gets done in accordance with principles that the sociologist Max Weber first codified: standardization, formalization, expert officialdom, specialization, hierarchy, and accountability. Add those up and you arrive at a system that values the written word; that is siloed because that’s what specialization does; that can sometimes be slow because there is a chain of command and an approval process. Standardization supports the value that it doesn’t matter who you are, who you know, what you look like when you’re applying for a permit, or who is issuing the permit: the case will be evaluated based on its merits. That is a good thing. Bureaucracy is a way to do business in a rational, impersonal, responsible and efficient way, at least in theory…
It becomes a problem when organizations start to violate their own values and lose connection with their purpose. If standardization turns into rigidity, doing justice to extenuating individual circumstances becomes hard. If formalization becomes pointless paper pushing, it defeats the purpose. And if accountability structures favor risk aversion over taking initiative, organizations can’t innovate.
Bureaucratic dysfunction occurs when the system that we’ve created ceases to produce the value that we wanted out of it. But that does not mean we have to throw away the baby with the bathwater. Can we create organizations that have the benefits of accountability, standardization and specialization without the burdens of slowness, rigidity, and silos? My answer is yes. Research we did with the Bloomberg Harvard City Leadership Initiative shows how organizations can improve performance by building capabilities that make them more nimble, responsive, and user-friendly. Cities that leverage data to better understand the communities they serve and measure performance learn and improve faster. Cities that use design thinking to reinvent resident services save time and money. And cities that collaborate across organizational and sector boundaries come up with more effective solutions to urban problems…(More)”
Researching data discomfort: The case of Statistics Norway’s quest for billing data
Paper by Lisa Reutter: “National statistics offices are increasingly exploring the possibilities of utilizing new data sources to position themselves in emerging data markets. In 2022, Statistics Norway announced that the national agency will require the biggest grocers in Norway to hand over all collected billing data to produce consumer behavior statistics which had previously been produced by other sampling methods. An online article discussing this proposal sparked a surprisingly (at least to Statistics Norway) high level of interest among readers, many of whom expressed concerns about this intended change in data practice. This paper focuses on the multifaceted online discussions of the proposal, as these enable us to study citizens’ reactions and feelings towards increased data collection and emerging public-private data flows in a Nordic context. Through an explorative empirical analysis of comment sections, this paper investigates what is discussed by commenters and reflects upon why this case sparked so much interest among citizens in the first place. It therefore contributes to the growing literature of citizens’ voices in data-driven administration and to a wider discussion on how to research public feeling towards datafication. I argue that this presents an interesting case of discomfort voiced by citizens, which demonstrates the contested nature of data practices among citizens–and their ability to regard data as deeply intertwined with power and politics. This case also reminds researchers to pay attention to seemingly benign and small changes in administration beyond artificial intelligence…(More)”
Oxford Intersections: AI in Society
Series edited by Philipp Hacker: “…provides an interdisciplinary corpus for understanding artificial intelligence (AI) as a global phenomenon that transcends geographical and disciplinary boundaries. Edited by a consortium of experts hailing from diverse academic traditions and regions, the 11 edited and curated sections provide a holistic view of AI’s societal impact. Critically, the work goes beyond the often Eurocentric or U.S.-centric perspectives that dominate the discourse, offering nuanced analyses that encompass the implications of AI for a range of regions of the world. Taken together, the sections of this work seek to move beyond the state of the art in three specific respects. First, they venture decisively beyond existing research efforts to develop a comprehensive account and framework for the rapidly growing importance of AI in virtually all sectors of society. Going beyond a mere mapping exercise, the curated sections assess opportunities, critically discuss risks, and offer solutions to the manifold challenges AI harbors in various societal contexts, from individual labor to global business, law and governance, and interpersonal relationships. Second, the work tackles specific societal and regulatory challenges triggered by the advent of AI and, more specifically, large generative AI models and foundation models, such as ChatGPT or GPT-4, which have so far received limited attention in the literature, particularly in monographs or edited volumes. Third, the novelty of the project is underscored by its decidedly interdisciplinary perspective: each section, whether covering Conflict; Culture, Art, and Knowledge Work; Relationships; or Personhood—among others—will draw on various strands of knowledge and research, crossing disciplinary boundaries and uniting perspectives most appropriate for the context at hand…(More)”.
Legal frictions for data openness
Paper by Ramya Chandrasekhar: “investigates legal entanglements of re-use, when data and content from the open web is used to train foundation AI models. Based on conversations with AI researchers and practitioners, an online workshop, and legal analysis of a repository of 41 legal disputes relating to copyright and data protection, this report highlights tensions between legal imaginations of data flows and computational processes involved in training foundation models.
To realise the promise of the open web as open for all, this report argues that efforts oriented solely towards techno-legal openness of training datasets are not enough. Techno-legal openness of datasets facilitates easy re-use of data. But, certain well-resourced actors like Big Tech are able to take advantage of data flows on the open web to internet to train proprietary foundation models, while giving little to no value back to either the maintenance of shared informational resources or communities of commoners. At the same time, open licenses no longer accommodate changing community preferences of sharing and re-use of data and content.
In addition to techno-legal openness of training datasets, there is a need for certain limits on the extractive power of well-resourced actors like BigTech combined with increased recognition of community data sovereignty. Alternative licensing frameworks, such as the Nwulite Obodo License, Kaitiakitanga Licenses, the Montreal License, the OpenRAIL Licenses, the Open Data Commons License, and the AI2Impact Licenses hold valuable insights in this regard. While these licensing frameworks impose more obligations on re-users and necessitate more collective thinking on interoperability,they are nonetheless necessary for the creation of healthy digital and data commons, to realise the original promise of the open web as open for all…(More)”.