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)”.

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)”.

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)”.

Integrating Data Governance and Mental Health Equity: Insights from ‘Towards a Set of Universal Data Principles’

Curated on Posted on by Stefaan Verhulst

Article by Cindy Hansen: “This recent scholarly work, “Towards a Set of Universal Data Principles” by Steve MacFeely et al (2025), delves comprehensively into the expansive landscape of data management and governance. It is noteworthy to acknowledge the intricate processes through which humans collect, manage, and disseminate vast quantities of data. …To truly democratize digital mental healthcare, it’s crucial to empower individuals in their data journey. By focusing on Digital Self-Determination, people can participate in a transformative shift where control over personal data becomes a fundamental right, aligning with the proposed universal data principles. One can envision a world where mental health data, collected and used responsibly, contributes not only to personal well-being but also to the greater public good, echoing the need for data governance to serve society at large.

This concept of digital self-determination empowers individuals by ensuring they have the autonomy to decide who accesses their mental health data and how it’s utilized. Such empowerment is especially significant in the context of mental health, where data sensitivity is high, and privacy is paramount. Giving people the confidence to manage their data fosters trust and encourages them to engage more openly with digital health services, promoting a culture of trust which is a core element of the proposed data governance frameworks.

Holistic Research Canada’s Outcome Monitoring System honors this ethos, allowing individuals to control how their data is accessed, shared, and used while maintaining engagement with healthcare providers. With this system, people can actively participate in their mental health decisions, supported by data that offers transparency about their progress and prognoses, which is crucial in realizing the potential of data to serve both individual and broader societal interests.

Furthermore, this tool provides actionable insights into mental health journeys, promoting evidence-based practices, enhancing transparency, and ensuring that individuals’ rights are safeguarded throughout. These principles are vital to transforming individuals from passive subjects into active stewards of their data, consistent with the proposed principles of safeguarding data quality, integrity, and security…(More)”.

‘We are flying blind’: RFK Jr.’s cuts halt data collection on abortion, cancer, HIV and more

Curated on Posted on by Stefaan Verhulst

Article by Alice Miranda Ollstein: “The federal teams that count public health problems are disappearing — putting efforts to solve those problems in jeopardy.

Health Secretary Robert F. Kennedy Jr.’s purge of tens of thousands of federal workers has halted efforts to collect data on everything from cancer rates in firefighters to mother-to-baby transmission of HIV and syphilis to outbreaks of drug-resistant gonorrhea to cases of carbon monoxide poisoning.

The cuts threaten to obscure the severity of pressing health threats and whether they’re getting better or worse, leaving officials clueless on how to respond. They could also make it difficult, if not impossible, to assess the impact of the administration’s spending and policies. Both outside experts and impacted employees argue the layoffs will cost the government more money in the long run by eliminating information on whether programs are effective or wasteful, and by allowing preventable problems to fester.

“Surveillance capabilities are crucial for identifying emerging health issues, directing resources efficiently, and evaluating the effectiveness of existing policies,” said Jerome Adams, who served as surgeon general in the first Trump’s administration. “Without robust data and surveillance systems, we cannot accurately assess whether we are truly making America healthier.”..(More)”.

The Future of Health Is Preventive — If We Get Data Governance Right

Curated on Posted on by Stefaan Verhulst

Article by Stefaan Verhulst: “After a long gestation period of three years, the European Health Data Space (EHDS) is now coming into effect across the European Union, potentially ushering in a new era of health data access, interoperability, and innovation. As this ambitious initiative enters the implementation phase, it brings with it the opportunity to fundamentally reshape how health systems across Europe operate. More generally, the EHDS contains important lessons (and some cautions) for the rest of the world, suggesting how a fragmented, reactive model of healthcare may transition to one that is more integrated, proactive, and prevention-oriented.

For too long, health systems–in the EU and around the world–have been built around treating diseases rather than preventing them. Now, we have an opportunity to change that paradigm. Data, and especially the advent of AI, give us the tools to predict and intervene before illness takes hold. Data offers the potential for a system that prioritizes prevention–one where individuals receive personalized guidance to stay healthy, policymakers access real-time evidence to address risks before they escalate, and epidemics are predicted weeks in advance, enabling proactive, rapid, and highly effective responses.

But to make AI-powered preventive health care a reality, and to make the EHDS a success, we need a new data governance approach, one that would include two key components:

  • The ability to reuse data collected for other purposes (e.g., mobility, retail sales, workplace trends) to improve health outcomes.
  • The ability to integrate different data sources–clinical records and electronic health records (EHRS), but also environmental, social, and economic data — to build a complete picture of health risks.

In what follows, we outline some critical aspects of this new governance framework, including responsible data access and reuse (so-called secondary use), moving beyond traditional consent models to a social license for reuse, data stewardship, and the need to prioritize high-impact applications. We conclude with some specific recommendations for the EHDS, built from the preceding general discussion about the role of AI and data in preventive health…(More)”.

Can Real-Time Metrics Fill China’s Data Gap?

Curated on Posted on by Stefaan Verhulst

Case-study by Danielle Goldfarb: “After Chinese authorities abruptly reversed the country’s zero-COVID policy in 2022, global policymakers needed a clear and timely picture of the economic and health fallout.

China’s economy is the world’s second largest and the country has deep global links, so an accurate picture of its trajectory mattered for global health, growth and inflation. Getting a solid read was a challenge, however, since official health and economic data not only were not timely, but were widely viewed as unreliable.

There are now vast amounts and varied types of digital data available, from satellite images to social media text to online payments; these, along with advances in artificial intelligence (AI), make it possible to collect and analyze digital data in ways previously impossible.

Could these new tools help governments and global institutions refute or confirm China’s official picture and gather more timely intelligence?..(More)”.

Artificial intelligence for modelling infectious disease epidemics

Curated on Posted on by Stefaan Verhulst

Paper by Moritz U. G. Kraemer et al: “Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI…(More)”.

Announcing the Youth Engagement Toolkit for Responsible Data Reuse: An Innovative Methodology for the Future of Data-Driven Services

Curated on Posted on by Stefaan Verhulst

Blog by Elena Murray, Moiz Shaikh, and Stefaan G. Verhulst: “Young people seeking essential services — whether mental health support, education, or government benefits — often face a critical challenge: they are asked to share their data without having a say in how it is used or for what purpose. While the responsible use of data can help tailor services to better meet their needs and ensure that vulnerable populations are not overlooked, a lack of trust in data collection and usage can have the opposite effect.

When young people feel uncertain or uneasy about how their data is being handled, they may adopt privacy-protective behaviors — choosing not to seek services at all or withholding critical information out of fear of misuse. This risks deepening existing inequalities rather than addressing them.

To build trust, those designing and delivering services must engage young people meaningfully in shaping data practices. Understanding their concerns, expectations, and values is key to aligning data use with their preferences. But how can this be done effectively?

This question was at the heart of a year-long global collaboration through the NextGenData project, which brought together partners worldwide to explore solutions. Today, we are releasing a key deliverable of that project: The Youth Engagement Toolkit for Responsible Data Reuse:

Based on a methodology developed and piloted during the NextGenData project, the Toolkit describes an innovative methodology for engaging young people on responsible data reuse practices, to improve services that matter to them…(More)”.