Paper by Rashid Mushkani, Hugo Berard, Allison Cohen, Shin Koeski: “This paper proposes a Right to AI, which asserts that individuals and communities should meaningfully participate in the development and governance of the AI systems that shape their lives. Motivated by the increasing deployment of AI in critical domains and inspired by Henri Lefebvre’s concept of the Right to the City, we reconceptualize AI as a societal infrastructure, rather than merely a product of expert design. In this paper, we critically evaluate how generative agents, large-scale data extraction, and diverse cultural values bring new complexities to AI oversight. The paper proposes that grassroots participatory methodologies can mitigate biased outcomes and enhance social responsiveness. It asserts that data is socially produced and should be managed and owned collectively. Drawing on Sherry Arnstein’s Ladder of Citizen Participation and analyzing nine case studies, the paper develops a four-tier model for the Right to AI that situates the current paradigm and envisions an aspirational future. It proposes recommendations for inclusive data ownership, transparent design processes, and stakeholder-driven oversight. We also discuss market-led and state-centric alternatives and argue that participatory approaches offer a better balance between technical efficiency and democratic legitimacy…(More)”.
The world at our fingertips, just out of reach: the algorithmic age of AI
Article by Soumi Banerjee: “Artificial intelligence (AI) has made global movements, testimonies, and critiques seem just a swipe away. The digital realm, powered by machine learning and algorithmic recommendation systems, offers an abundance of visual, textual, and auditory information. With a few swipes or keystrokes, the unbounded world lies open before us. Yet this ‘openness’ conceals a fundamental paradox: the distinction between availability and accessibility.
What is technically available is not always epistemically accessible. What appears global is often algorithmically curated. And what is served to users under the guise of choice frequently reflects the imperatives of engagement, profit, and emotional resonance over critical understanding or cognitive expansion.
The transformative potential of AI in democratising access to information comes with risks. Algorithmic enclosure and content curation can deepen epistemic inequality, particularly for the youth, whose digital fluency often masks a lack of epistemic literacy. What we need is algorithmic transparency, civic education in media literacy, and inclusive knowledge formats…(More)”.
How Bad Is China’s Economy? The Data Needed to Answer Is Vanishing
Article by Rebecca Feng and Jason Douglas: “Not long ago, anyone could comb through a wide range of official data from China. Then it started to disappear.
Land sales measures, foreign investment data and unemployment indicators have gone dark in recent years. Data on cremations and a business confidence index have been cut off. Even official soy sauce production reports are gone.
In all, Chinese officials have stopped publishing hundreds of data points once used by researchers and investors, according to a Wall Street Journal analysis.
In most cases, Chinese authorities haven’t given any reason for ending or withholding data. But the missing numbers have come as the world’s second biggest economy has stumbled under the weight of excessive debt, a crumbling real-estate market and other troubles—spurring heavy-handed efforts by authorities to control the narrative.China’s National Bureau of Statistics stopped publishing some numbers related to unemployment in urban areas in recent years. After an anonymous user on the bureau’s website asked why one of those data points had disappeared, the bureau said only that the ministry that provided it stopped sharing the data.
The disappearing data have made it harder for people to know what’s going on in China at a pivotal time, with the trade war between Washington and Beijing expected to hit China hard and weaken global growth. Plunging trade with the U.S. has already led to production shutdowns and job cuts.
Getting a true read on China’s growth has always been tricky. Many economists have long questioned the reliability of China’s headline gross domestic product data, and concerns have intensified recently. Official figures put GDP growth at 5% last year and 5.2% in 2023, but some have estimated that Beijing overstated its numbers by as much as 2 to 3 percentage points.
To get what they consider to be more realistic assessments of China’s growth, economists have turned to alternative sources such as movie box office revenues, satellite data on the intensity of nighttime lights, the operating rates of cement factories and electricity generation by major power companies. Some parse location data from mapping services run by private companies such as Chinese tech giant Baidu to gauge business activity.
One economist said he has been assessing the health of China’s services sector by counting news stories about owners of gyms and beauty salons who abruptly close up and skip town with users’ membership fees…(More)”.
Governing in the Age of AI: Reimagining Local Government
Report by the Tony Blair Institute for Global Change: “…The limits of the existing operating model have been reached. Starved of resources by cuts inflicted by previous governments over the past 15 years, many councils are on the verge of bankruptcy even though local taxes are at their highest level. Residents wait too long for care, too long for planning applications and too long for benefits; many people never receive what they are entitled to. Public satisfaction with local services is sliding.
Today, however, there are new tools – enabled by artificial intelligence – that would allow councils to tackle these challenges. The day-to-day tasks of local government, whether related to the delivery of public services or planning for the local area, can all be performed faster, better and cheaper with the use of AI – a true transformation not unlike the one seen a century ago.
These tools would allow councils to overturn an operating model that is bureaucratic, labour-intensive and unresponsive to need. AI could release staff from repetitive tasks and relieve an overburdened and demotivated workforce. It could help citizens navigate the labyrinth of institutions, webpages and forms with greater ease and convenience. It could support councils to make better long-term decisions to drive economic growth, without which the resource pressure will only continue to build…(More)”.
Co-Designing AI Systems with Value-Sensitive Citizen Science
Paper by Sachit Mahajan and Dirk Helbing: “As artificial intelligence (AI) systems increasingly shape everyday life, integrating diverse community values into their development becomes both an ethical imperative and a practical necessity. This paper introduces Value Sensitive Citizen Science (VSCS), a systematic framework combining Value Sensitive Design (VSD) principles with citizen science methods to foster meaningful public participation in AI. Addressing critical gaps in existing approaches, VSCS integrates culturally grounded participatory methods and structured cognitive scaffolding through the Participatory Value-Cognition Taxonomy (PVCT). Through iterative value-sensitive participation cycles guided by an extended scenario logic (What-if, If-then, Then-what, What-now), community members act as genuine coresearchers-identifying, translating, and operationalizing local values into concrete technical requirements. The framework also institutionalizes governance structures for ongoing oversight, adaptability, and accountability across the AI lifecycle. By explicitly bridging participatory design with algorithmic accountability, VSCS ensures that AI systems reflect evolving community priorities rather than reinforcing top-down or monocultural perspectives. Critical discussions highlight VSCS’s practical implications, addressing challenges such as power dynamics, scalability, and epistemic justice. The paper concludes by outlining actionable strategies for policymakers and practitioners, alongside future research directions aimed at advancing participatory, value-driven AI development across diverse technical and sociocultural contexts…(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)”
Glorious RAGs : A Safer Path to Using AI in the Social Sector
Blog by Jim Fruchterman: “Social sector leaders ask me all the time for advice on using AI. As someone who started for-profit machine learning (AI) companies in the 1980s, but then pivoted to running nonprofit social enterprises, I’m often the first person from Silicon Valley that many nonprofit leaders have met. I joke that my role is often that of “anti-consultant,” talking leaders out of doing an app, a blockchain (smile) or firing half their staff because of AI. Recently, much of my role has been tamping down the excessive expectations being bandied about for the impact of AI on organizations. However, two years into the latest AI fad wave created by ChatGPT and its LLM (large language model) peers, more and more of the leaders are describing eminently sensible applications of LLMs to their programs. The most frequent of these approaches can be described as variations on “Retrieval-Augmented Generation,” also known as RAG. I am quite enthusiastic about using RAG for social impact, because it addresses a real need and supplies guardrails for using LLMs effectively…(More)”
Understanding and Addressing Misinformation About Science
Report by National Academies of Sciences, Engineering, and Medicine: “Our current information ecosystem makes it easier for misinformation about science to spread and harder for people to figure out what is scientifically accurate. Proactive solutions are needed to address misinformation about science, an issue of public concern given its potential to cause harm at individual, community, and societal levels. Improving access to high-quality scientific information can fill information voids that exist for topics of interest to people, reducing the likelihood of exposure to and uptake of misinformation about science. Misinformation is commonly perceived as a matter of bad actors maliciously misleading the public, but misinformation about science arises both intentionally and inadvertently and from a wide range of sources…(More)”.
Bad Public Policy: Malignity, Volatility and the Inherent Vices of Policymaking
Book by Policy studies assume the existence of baseline parameters – such as honest governments doing their best to create public value, publics responding in good faith, and both parties relying on a policy-making process which aligns with the public interest. In such circumstances, policy goals are expected to be produced through mechanisms in which the public can articulate its preferences and policy-makers are expected to listen to what has been said in determining their governments’ courses of action. While these conditions are found in some governments, there is evidence from around the world that much policy-making occurs without these pre-conditions and processes. Unlike situations which produce what can be thought of as ‘good’ public policy, ‘bad’ public policy is a more common outcome. How this happens and what makes for bad public policy are the subjects of this Element…(More)”.
Rebooting the global consensus: Norm entrepreneurship, data governance and the inalienability of digital bodies
Paper by Siddharth Peter de Souza and Linnet Taylor: “The establishment of norms among states is a common way of governing international actions. This article analyses the potential of norm-building for governing data and artificial intelligence technologies’ collective effects. Rather than focusing on state actors’s ability to establish and enforce norms, however, we identify a contrasting process taking place among civil society organisations in response to the international neoliberal consensus on the commodification of data. The norm we identify – ‘nothing about us without us’ – asserts civil society’s agency, and specifically the right of those represented in datasets to give or refuse permission through structures of democratic representation. We argue that this represents a form of norm-building that should be taken as seriously as that of states, and analyse how it is constructing the political power, relations, and resources to engage in governing technology at scale. We first outline how this counter-norming is anchored in data’s connections to bodies, land, community, and labour. We explore the history of formal international norm-making and the current norm-making work being done by civil society organisations internationally, and argue that these, although very different in their configurations and strategies, are comparable in scale and scope. Based on this, we make two assertions: first, that a norm-making lens is a useful way for both civil society and research to frame challenges to the primacy of market logics in law and governance, and second, that the conceptual exclusion of civil society actors as norm-makers is an obstacle to the recognition of counter-power in those spheres…(More)”.