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)”.
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)”.
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)”
A matter of choice: People and possibilities in the age of AI
UNDP Human Development Report 2025: “Artificial intelligence (AI) has broken into a dizzying gallop. While AI feats grab headlines, they privilege technology in a make-believe vacuum, obscuring what really matters: people’s choices.
The choices that people have and can realize, within ever expanding freedoms, are essential to human development, whose goal is for people to live lives they value and have reason to value. A world with AI is flush with choices the exercise of which is both a matter of human development and a means to advance it.
Going forward, development depends less on what AI can do—not on how human-like it is perceived to be—and more on mobilizing people’s imaginations to reshape economies and societies to make the most of it. Instead of trying vainly to predict what will happen, this year’s Human Development Report asks what choices can be made so that new development pathways for all countries dot the horizon, helping everyone have a shot at thriving in a world with AI…(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)”.
Smart Cities:Technologies and Policy Options to Enhance Services and Transparency
GAO Report: “Cities across the nation are using “smart city” technologies like traffic cameras and gunshot detectors to improve public services. In this technology assessment, we looked at their use in transportation and law enforcement.
Experts and city officials reported multiple benefits. For example, Houston uses cameras and Bluetooth sensors to measure traffic flow and adjust signal timing. Other cities use license plate readers to find stolen vehicles.
But the technologies can be costly and the benefits unclear. The data they collect may be sold, raising privacy and civil liberties concerns. We offer three policy options to address such challenges…(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)”.
AI action plan database
A project by the Institute for Progress: “In January 2025, President Trump tasked the Office of Science and Technology Policy with creating an AI Action Plan to promote American AI Leadership. The government requested input from the public, and received 10,068 submissions. The database below summarizes specific recommendations from these submissions. … We used AI to extract recommendations from each submission, and to tag them with relevant information. Click on a recommendation to learn more about it. See our analysis of common themes and ideas across these recommendations…(More)”.
Technical Tiers: A New Classification Framework for Global AI Workforce Analysis
Report by Siddhi Pal, Catherine Schneider and Ruggero Marino Lazzaroni: “… introduces a novel three-tiered classification system for global AI talent that addresses significant methodological limitations in existing workforce analyses, by distinguishing between different skill categories within the existing AI talent pool. By distinguishing between non-technical roles (Category 0), technical software development (Category 1), and advanced deep learning specialization (Category 2), our framework enables precise examination of AI workforce dynamics at a pivotal moment in global AI policy.
Through our analysis of a sample of 1.6 million individuals in the AI talent pool across 31 countries, we’ve uncovered clear patterns in technical talent distribution that significantly impact Europe’s AI ambitions. Asian nations hold an advantage in specialized AI expertise, with South Korea (27%), Israel (23%), and Japan (20%) maintaining the highest proportions of Category 2 talent. Within Europe, Poland and Germany stand out as leaders in specialized AI talent. This may be connected to their initiatives to attract tech companies and investments in elite research institutions, though further research is needed to confirm these relationships.
Our data also reveals a shifting landscape of global talent flows. Research shows that countries employing points-based immigration systems attract 1.5 times more high-skilled migrants than those using demand-led approaches. This finding takes on new significance in light of recent geopolitical developments affecting scientific research globally. As restrictive policies and funding cuts create uncertainty for researchers in the United States, one of the big destinations for European AI talent, the way nations position their regulatory environments, scientific freedoms, and research infrastructure will increasingly determine their ability to attract and retain specialized AI talent.
The gender analysis in our study illuminates another dimension of competitive advantage. Contrary to the overall AI talent pool, EU countries lead in female representation in highly technical roles (Category 2), occupying seven of the top ten global rankings. Finland, Czechia, and Italy have the highest proportion of female representation in Category 2 roles globally (39%, 31%, and 28%, respectively). This gender diversity represents not merely a social achievement but a potential strategic asset in AI innovation, particularly as global coalitions increasingly emphasize the importance of diverse perspectives in AI development…(More)”