Building Community-Centered AI Collaborations

Curated on Posted on by Stefaan Verhulst

Article by Michelle Flores Vryn and Meena Das: “AI can only boost the under-resourced nonprofit world if we design it to serve the communities we care about. But as nonprofits consider how to incorporate AI into their work, many look to expertise from tech sector, expecting tools and implementation advice as well as ethical guidance. Yet when mission-driven entities—with a strong focus on people, communities, and equity—partner solely with tech companies, they may encounter a variety of obstacles, such as:

  1. Limited understanding of community needs: Sector-specific knowledge is essential for aligning AI with nonprofit missions, something many tech companies lack.
  2. Bias in AI models: Without diverse input, AI models may exacerbate biases or misrepresent the communities that nonprofits serve.
  3. Resource constraints: Tech solutions often presume budgets or capacity beyond what nonprofits can bring to bear, creating a reliance on tools that fit the nonprofit context.

We need creative, diverse collaborations across various fields to ensure that technology is deployed in ways that align with nonprofit values, build trust, and serve the greater good. Seeking partners outside of the tech world helps nonprofits develop AI solutions that are context-aware, equitable, and resource-sensitive. Most importantly, nonprofit practitioners must deeply consider our ideal future state: What does an AI-empowered nonprofit sector look like when it truly centers human well-being, community agency, and ethical technology?

Imagining this future means not just reacting to emerging technology but proactively shaping its trajectory. Instead of simply adapting to AI’s capabilities, nonprofits should ask:

  • What problems do we truly need AI to solve?
  • Whose voices must be centered in AI decision-making?
  • How do we ensure AI remains a tool for empowerment rather than control?..(More)”.

Policy Implications of DeepSeek AI’s Talent Base

Curated on Posted on by Stefaan Verhulst

Brief by Amy Zegart and Emerson Johnston: “Chinese startup DeepSeek’s highly capable R1 and V3 models challenged prevailing beliefs about the United States’ advantage in AI innovation, but public debate focused more on the company’s training data and computing power than human talent. We analyzed data on the 223 authors listed on DeepSeek’s five foundational technical research papers, including information on their research output, citations, and institutional affiliations, to identify notable talent patterns. Nearly all of DeepSeek’s researchers were educated or trained in China, and more than half never left China for schooling or work. Of the quarter or so that did gain some experience in the United States, most returned to China to work on AI development there. These findings challenge the core assumption that the United States holds a natural AI talent lead. Policymakers need to reinvest in competing to attract and retain the world’s best AI talent while bolstering STEM education to maintain competitiveness…(More)”.

How Bad Is China’s Economy? The Data Needed to Answer Is Vanishing

Curated on Posted on by Stefaan Verhulst

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)”.

The Dangers of AI Nationalism and Beggar-Thy-Neighbour Policies

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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:
    1. OMB could issue guidance to authorize fraud prevention as a routine use in System of Records Notices.
    2. Congress could enact special authorities or exemptions for data sharing that supports program integrity and fraud prevention.
    3. 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

Curated on Posted on by Stefaan Verhulst

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)”

Smart Cities:Technologies and Policy Options to Enhance Services and Transparency

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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)”