Understanding the Impacts of Generative AI Use on Children

Curated on Posted on by Stefaan Verhulst

Primer by The Alan Turing Institute and LEGO Foundation: “There is a growing body of research looking at the potential positive and negative impacts of generative AI and its associated risks. However, there is a lack of research that considers the potential impacts of these technologies on children, even though generative AI is already being deployed within many products and systems that children engage with, from games to educational platforms. Children have particular needs and rights that must be accounted for when designing, developing, and rolling out new technologies, and more focus on children’s rights is needed. While children are the group that may be most impacted by the widespread deployment of generative AI, they are simultaneously the group least represented in decision-making processes relating to the design, development, deployment or governance of AI. The Alan Turing Institute’s Children and AI and AI for Public Services teams explored the perspectives of children, parents, carers and teachers on generative AI technologies. Their research is guided by the ‘Responsible Innovation in Technology for Children’ (RITEC) framework for digital technology, play and children’s wellbeing established by UNICEF and funded by the LEGO Foundation and seeks to examine the potential impacts of generative AI on children’s wellbeing. The utility of the RITEC framework is that it allows for the qualitative analysis of wellbeing to take place by foregrounding more specific factors such as identity and creativity, which are further explored in each of the work packages.

The project provides unique and much needed insights into impacts of generative AI on children through combining quantitative and qualitative research methods…(More)”.

Comparative evaluation of behavioral epidemic models using COVID-19 data

Curated on Posted on by Stefaan Verhulst

Paper by Nicolò Gozzi, Nicola Perra, and Alessandro Vespignani: “Characterizing the feedback linking human behavior and the transmission of infectious diseases (i.e., behavioral changes) remains a significant challenge in computational and mathematical epidemiology. Existing behavioral epidemic models often lack real-world data calibration and cross-model performance evaluation in both retrospective analysis and forecasting. In this study, we systematically compare the performance of three mechanistic behavioral epidemic models across nine geographies and two modeling tasks during the first wave of COVID-19, using various metrics. The first model, a Data-Driven Behavioral Feedback Model, incorporates behavioral changes by leveraging mobility data to capture variations in contact patterns. The second and third models are Analytical Behavioral Feedback Models, which simulate the feedback loop either through the explicit representation of different behavioral compartments within the population or by utilizing an effective nonlinear force of infection. Our results do not identify a single best model overall, as performance varies based on factors such as data availability, data quality, and the choice of performance metrics. While the Data-Driven Behavioral Feedback Model incorporates substantial real-time behavioral information, the Analytical Compartmental Behavioral Feedback Model often demonstrates superior or equivalent performance in both retrospective fitting and out-of-sample forecasts. Overall, our work offers guidance for future approaches and methodologies to better integrate behavioral changes into the modeling and projection of epidemic dynamics…(More)”.

Fixing the US statistical infrastructure

Curated on Posted on by Stefaan Verhulst

Article by Nancy Potok and Erica L. Groshen: “Official government statistics are critical infrastructure for the information age. Reliable, relevant, statistical information helps businesses to invest and flourish; governments at the local, state, and national levels to make critical decisions on policy and public services; and individuals and families to invest in their futures. Yet surrounded by all manner of digitized data, one can still feel inadequately informed. A major driver of this disconnect in the US context is delayed modernization of the federal statistical system. The disconnect will likely worsen in coming months as the administration shrinks statistical agencies’ staffing, terminates programs (notably for health and education statistics), and eliminates unpaid external advisory groups. Amid this upheaval, might the administration’s appetite for disruption be harnessed to modernize federal statistics?

Federal statistics, one of the United States’ premier public goods, differ from privately provided data because they are privacy protected, aggregated to address relevant questions for decision-makers, constructed transparently, and widely available without a subscription. The private sector cannot be expected to adequately supply such statistical infrastructure. Yes, some companies collect and aggregate some economic data, such as credit card purchases and payroll information. But without strong underpinnings of a modern, federal information infrastructure, there would be large gaps in nationally consistent, transparent, trustworthy data. Furthermore, most private providers rely on public statistics for their internal analytics, to improve their products. They are among the many data users asking for more from statistical agencies…(More)”.

Generative AI Outlook Report

Curated on Posted on by Stefaan Verhulst

Outlook report, prepared by the European Commission’s Joint Research Centre (JRC): “…examines the transformative role of Generative AI (GenAI) with a specific emphasis on the European Union. It highlights the potential of GenAI for innovation, productivity, and societal change. GenAI is a disruptive technology due to its capability of producing human-like content at an unprecedented scale. As such, it holds multiple opportunities for advancements across various sectors, including healthcare, education, science, and creative industries. At the same time, GenAI also presents significant challenges, including the possibility to amplify misinformation, bias, labour disruption, and privacy concerns. All those issues are cross-cutting and therefore, the rapid development of GenAI requires a multidisciplinary approach to fully understand its implications. Against this context, the Outlook report begins with an overview of the technological aspects of GenAI, detailing their current capabilities and outlining emerging trends. It then focuses on economic implications, examining how GenAI can transform industry dynamics and necessitate adaptation of skills and strategies. The societal impact of GenAI is also addressed, with focus on both the opportunities for inclusivity and the risks of bias and over-reliance. Considering these challenges, the regulatory framework section outlines the EU’s current legislative framework, such as the AI Act and horizontal Data legislation to promote trustworthy and transparent AI practices. Finally, sector-specific ‘deep dives’ examine the opportunities and challenges that GenAI presents. This section underscores the need for careful management and strategic policy interventions to maximize its potential benefits while mitigating the risks. The report concludes that GenAI has the potential to bring significant social and economic impact in the EU, and that a comprehensive and nuanced policy approach is needed to navigate the challenges and opportunities while ensuring that technological developments are fully aligned with democratic values and EU legal framework…(More)”.

A New Paradigm for Fueling AI for the Public Good

Curated on Posted on by Stefaan Verhulst

Article by Kevin T. Frazier: “Imagine receiving this email in the near future: “Thank you for sharing data with the American Data Collective on May 22, 2025. After first sharing your workout data with SprintAI, a local startup focused on designing shoes for differently abled athletes, your data donation was also sent to an artificial intelligence research cluster hosted by a regional university. Your donation is on its way to accelerate artificial intelligence innovation and support researchers and innovators addressing pressing public needs!”

That is exactly the sort of message you could expect to receive if we made donations of personal data akin to blood donations—a pro-social behavior that may not immediately serve a donor’s individual needs but may nevertheless benefit the whole of the community. This vision of a future where data flow toward the public good is not science fiction—it is a tangible possibility if we address a critical bottleneck faced by innovators today.

Creating the data equivalent of blood banks may not seem like a pressing need or something that people should voluntarily contribute to, given widespread concerns about a few large artificial intelligence (AI) companies using data for profit-driven and, arguably, socially harmful ends. This narrow conception of the AI ecosystem fails to consider the hundreds of AI research initiatives and startups that have a desperate need for high-quality data. I was fortunate enough to meet leaders of those nascent AI efforts at Meta’s Open Source AI Summit in Austin, Texas. For example, I met with Matt Schwartz, who leads a startup that leans on AI to glean more diagnostic information from colonoscopies. I also connected with Edward Chang, a professor of neurological surgery at the University of California, San Francisco Weill Institute for Neurosciences, who relies on AI tools to discover new information on how and why our brains work. I also got to know Corin Wagen, whose startup is helping companies “find better molecules faster.” This is a small sample of the people leveraging AI for objectively good outcomes. They need your help. More specifically, they need your data.

A tragic irony shapes our current data infrastructure. Most of us share mountains of data with massive and profitable private parties—smartwatch companies, diet apps, game developers, and social media companies. Yet, AI labs, academic researchers, and public interest organizations best positioned to leverage our data for the common good are often those facing the most formidable barriers to acquiring the necessary quantity, quality, and diversity of data. Unlike OpenAI, they are not going to use bots to scrape the internet for data. Unlike Google and Meta, they cannot rely on their own social media platforms and search engines to act as perpetual data generators. And, unlike Anthropic, they lack the funds to license data from media outlets. So, while commercial entities amass vast datasets, frequently as a byproduct of consumer services and proprietary data acquisition strategies, mission-driven AI initiatives dedicated to public problems find themselves in a state of chronic data scarcity. This is not merely a hurdle—it is a systemic bottleneck choking off innovation where society needs it most, delaying or even preventing the development of AI tools that could significantly improve lives.

Individuals are, quite rightly, increasingly hesitant to share their personal information, with concerns about privacy, security, and potential misuse being both rampant and frequently justified by past breaches and opaque practices. Yet, in a striking contradiction, troves of deeply personal data are continuously siphoned by app developers, by tech platforms, and, often opaquely, by an extensive network of data brokers. This practice often occurs with minimal transparency and without informed consent concerning the full lifecycle and downstream uses of that data. This lack of transparency extends to how algorithms trained on this data make decisions that can impact individuals’ lives—from loan applications to job prospects—often without clear avenues for recourse or understanding, potentially perpetuating existing societal biases embedded in historical data…(More)”.

Sentinel Cities for Public Health

Curated on Posted on by Stefaan Verhulst

Article by Jesse Rothman, Paromita Hore & Andrew McCartor: “In 2017, a New York City health inspector visited the home of a 5-year-old child with an elevated blood lead level. With no sign of lead paint—the usual suspect in such cases—the inspector discovered dangerous levels of lead in a bright yellow container of “Georgian Saffron,” a spice obtained in the family’s home country. It was not the first case associated with the use of lead-containing Georgian spices—the NYC Health Department shared their findings with authorities in Georgia, which catalyzed a survey of children’s blood lead levels in Georgia, and led to increased regulatory enforcement and education. Significant declines in spice lead levels in the country have had ripple effects in NYC also: not only a drop in spice samples from Georgia containing detectable lead but also a significant reduction in blood lead levels among NYC children of Georgian ancestry.

This wasn’t a lucky break—it was the result of a systematic approach to transform local detection into global impact. Findings from local NYC surveillance are, of course, not limited to Georgian spices. Surveillance activities have identified a variety of lead-containing consumer products from around the world, from cosmetics and medicines to ceramics and other goods. Routinely surveying local stores for lead-containing products has resulted in the removal of over 30,000 hazardous consumer products from NYC store shelves since 2010.

How can we replicate and scale up NYC’s model to address the global crisis of lead poisoning?…(More)”.

Energy and AI Observatory

Curated on Posted on by Stefaan Verhulst

IEA’s Energy and AI Observatory: “… provides up-to-date data and analysis on the growing links between the energy sector and artificial intelligence (AI). The new and fast-moving field of AI requires a new approach to gathering data and information, and the Observatory aims to provide regularly updated data and a comprehensive view of the implications of AI on energy demand (energy for AI) and of AI applications for efficiency, innovation, resilience and competitiveness in the energy sector (AI for energy). This first-of-a-kind platform is developed and maintained by the IEA, with valuable contributions of data and insights from the IEA’s energy industry and tech sector partners, and complements the IEA’s Special Report on Energy and AI…(More)”.

AI alone cannot solve the productivity puzzle

Curated on Posted on by Stefaan Verhulst

Article by Carl Benedikt Frey: “Each time fears of AI-driven job losses flare up, optimists reassure us that artificial intelligence is a productivity tool that will help both workers and the economy. Microsoft chief Satya Nadella thinks autonomous AI agents will allow users to name their goal while the software plans, executes and learns across every system. A dream tool — if efficiency alone was enough to solve the productivity problem.

History says it is not. Over the past half-century we have filled offices and pockets with ever-faster computers, yet labour-productivity growth in advanced economies has slowed from roughly 2 per cent a year in the 1990s to about 0.8 per cent in the past decade. Even China’s once-soaring output per worker has stalled.

The shotgun marriage of the computer and the internet promised more than enhanced office efficiency — it envisioned a golden age of discovery. By placing the world’s knowledge in front of everyone and linking global talent, breakthroughs should have multiplied. Yet research productivity has sagged. The average scientist now produces fewer breakthrough ideas per dollar than their 1960s counterpart.

What went wrong? As economist Gary Becker once noted, parents face a quality-versus-quantity trade-off: the more children they have, the less they can invest in each child. The same might be said for innovation.

Large-scale studies of inventive output confirm the result: researchers juggling more projects are less likely to deliver breakthrough innovations. Over recent decades, scientific papers and patents have become increasingly incremental. History’s greats understood why. Isaac Newton kept a single problem “constantly before me . . . till the first dawnings open slowly, by little and little, into a full and clear light”. Steve Jobs concurred: “Innovation is saying no to a thousand things.”

Human ingenuity thrives where precedent is thin. Had the 19th century focused solely on better looms and ploughs, we would enjoy cheap cloth and abundant grain — but there would be no antibiotics, jet engines or rockets. Economic miracles stem from discovery, not repeating tasks at greater speed.

Large language models gravitate towards the statistical consensus. A model trained before Galileo would have parroted a geocentric universe; fed 19th-century texts it would have proved human flight impossible before the Wright brothers succeeded. A recent Nature review found that while LLMs lightened routine scientific chores, the decisive leaps of insight still belonged to humans. Even Demis Hassabis, whose team at Google DeepMind produced AlphaFold — a model that can predict the shape of a protein and is arguably AI’s most celebrated scientific feat so far — admits that achieving genuine artificial general intelligence systems that can match or surpass humans across the full spectrum of cognitive tasks may require “several more innovations”…(More)”.

Facilitating the secondary use of health data for public interest purposes across borders

Curated on Posted on by Stefaan Verhulst

OECD Paper: “Recent technological developments create significant opportunities to process health data in the public interest. However, the growing fragmentation of frameworks applied to data has become a structural impediment to fully leverage these opportunities. Public and private stakeholders suggest that three key areas should be analysed to support this outcome, namely: the convergence of governance frameworks applicable to health data use in the public interest across jurisdictions; the harmonisation of national procedures applicable to secondary health data use; and the public perceptions around the use of health data. This paper explores each of these three key areas and concludes with an overview of collective findings relating specifically to the convergence of legal bases for secondary data use…(More)”.

Protecting young digital citizens

Curated on Posted on by Stefaan Verhulst

Blog by Pascale Raulin-Serrier: “…As digital tools become more deeply embedded in children’s lives, many young users are unaware of the long-term consequences of sharing personal information online through apps, games, social media platforms and even educational tools. The large-scale collection of data related to their preferences, identity or lifestyle may be used for targeted advertising or profiling. This affects not only their immediate online experiences but can also have lasting consequences, including greater risks of discrimination and exclusion. These concerns underscore the urgent need for stronger safeguards, greater transparency and a child-centered approach to data governance.

CNIL’s initiatives to promote children’s privacy

In response to these challenges, the CNIL introduced eight recommendations in 2021 to provide practical guidance for children, parents and other stakeholders in the digital economy. These are built around several key pillars to promote and protect children’s privacy:

1. Providing specific safeguards

Children have distinct digital rights and must be able to exercise them fully. Under the European General Data Protection Regulation (GDPR), they benefit from special protections, including the right to be forgotten and, in some cases, the ability to consent to the processing of their data.In France, children can only register for social networks or online gaming platforms if they are over 15, or with parental consent if they are younger. CNIL helps hold platforms accountable by offering clear recommendations on how to present terms of service and collect consent in ways that are accessible and understandable to children.

2. Balancing autonomy and protection

The needs and capacities of a 6-year-old child differ greatly from those of a 16-year-old adolescent. It is essential to consider this diversity in online behaviour, maturity and the evolving ability to make informed decisions. The CNIL emphasizes  the importance of offering children a digital environment that strikes a balance between protection and autonomy. It also advocates for digital citizenship education to empower young people with the tools they need to manage their privacy responsibly…(More)”. See also Responsible Data for Children.