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)”.

Presenting the StanDat database on international standards: improving data accessibility on marginal topics

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Article by Solveig Bjørkholt: “This article presents an original database on international standards, constructed using modern data gathering methods. StanDat facilitates studies into the role of standards in the global political economy by (1) being a source for descriptive statistics, (2) enabling researchers to assess scope conditions of previous findings, and (3) providing data for new analyses, for example the exploration of the relationship between standardization and trade, as demonstrated in this article. The creation of StanDat aims to stimulate further research into the domain of standards. Moreover, by exemplifying data collection and dissemination techniques applicable to investigating less-explored subjects in the social sciences, it serves as a model for gathering, systematizing, and sharing data in areas where information is plentiful yet not readily accessible for research…(More)”.

Diversifying Professional Roles in Data Science

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Policy Briefing by Emma Karoune and Malvika Sharan: The interdisciplinary nature of the data science workforce extends beyond the traditional notion of a “data scientist.” A successful data science team requires a wide range of technical expertise, domain knowledge and leadership capabilities. To strengthen such a team-based approach, this note recommends that institutions, funders and policymakers invest in developing and professionalising diverse roles, fostering a resilient data science ecosystem for the future. 


By recognising the diverse specialist roles that collaborate within interdisciplinary teams, organisations can leverage deep expertise across multiple skill sets, enhancing responsible decision-making and fostering innovation at all levels. Ultimately, this note seeks to shift the perception of data science professionals from the conventional view of individual data scientists to a competency-based model of specialist roles within a team, each essential to the success of data science initiatives…(More)”.

Future of AI Research

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Report by the Association for the Advancement of Artificial Intelligence:  “As AI capabilities evolve rapidly, AI research is also undergoing a fast and significant transformation along many dimensions, including its topics, its methods, the research community, and the working environment. Topics such as AI reasoning and agentic AI have been studied for decades but now have an expanded scope in light of current AI capabilities and limitations. AI ethics and safety, AI for social good, and sustainable AI have become central themes in all major AI conferences. Moreover, research on AI algorithms and software systems is becoming increasingly tied to substantial amounts of dedicated AI hardware, notably GPUs, which leads to AI architecture co-creation, in a way that is more prominent now than over the last 3 decades. Related to this shift, more and more AI researchers work in corporate environments, where the necessary hardware and other resources are more easily available, compared to academia, questioning the roles of academic AI research, student retention, and faculty recruiting. The pervasive use of AI in our daily lives and its impact on people, society, and the environment makes AI a socio-technical field of study, thus highlighting the need for AI researchers to work with experts from other disciplines, such as psychologists, sociologists, philosophers, and economists. The growing focus on emergent AI behaviors rather than on designed and validated properties of AI systems renders principled empirical evaluation more important than ever. Hence the need arises for well-designed benchmarks, test methodologies, and sound processes to infer conclusions from the results of computational experiments. The exponentially increasing quantity of AI research publications and the speed of AI innovation are testing the resilience of the peer-review system, with the immediate release of papers without peer-review evaluation having become widely accepted across many areas of AI research. Legacy and social media increasingly cover AI research advancements, often with contradictory statements that confuse the readers and blur the line between reality and perception of AI capabilities. All this is happening in a geo-political environment, in which companies and countries compete fiercely and globally to lead the AI race. This rivalry may impact access to research results and infrastructure as well as global governance efforts, underscoring the need for international cooperation in AI research and innovation.

In this overwhelming multi-dimensional and very dynamic scenario, it is important to be able to clearly identify the trajectory of AI research in a structured way. Such an effort can define the current trends and the research challenges still ahead of us to make AI more capable and reliable, so we can safely use it in mundane but also, most importantly, in high-stake scenarios.

This study aims to do this by including 17 topics related to AI research, covering most of the transformations mentioned above. Each chapter of the study is devoted to one of these topics, sketching its history, current trends and open challenges…(More)”.

AI could supercharge human collective intelligence in everything from disaster relief to medical research

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Article by Hao Cui and Taha Yasseri: “Imagine a large city recovering from a devastating hurricane. Roads are flooded, the power is down, and local authorities are overwhelmed. Emergency responders are doing their best, but the chaos is massive.

AI-controlled drones survey the damage from above, while intelligent systems process satellite images and data from sensors on the ground and air to identify which neighbourhoods are most vulnerable.

Meanwhile, AI-equipped robots are deployed to deliver food, water and medical supplies into areas that human responders can’t reach. Emergency teams, guided and coordinated by AI and the insights it produces, are able to prioritise their efforts, sending rescue squads where they’re needed most.

This is no longer the realm of science fiction. In a recent paper published in the journal Patterns, we argue that it’s an emerging and inevitable reality.

Collective intelligence is the shared intelligence of a group or groups of people working together. Different groups of people with diverse skills, such as firefighters and drone operators, for instance, work together to generate better ideas and solutions. AI can enhance this human collective intelligence, and transform how we approach large-scale crises. It’s a form of what’s called hybrid collective intelligence.

Instead of simply relying on human intuition or traditional tools, experts can use AI to process vast amounts of data, identify patterns and make predictions. By enhancing human decision-making, AI systems offer faster and more accurate insights – whether in medical research, disaster response, or environmental protection.

AI can do this, by for example, processing large datasets and uncovering insights that would take much longer for humans to identify. AI can also get involved in physical tasks. In manufacturing, AI-powered robots can automate assembly lines, helping improve efficiency and reduce downtime.

Equally crucial is information exchange, where AI enhances the flow of information, helping human teams coordinate more effectively and make data-driven decisions faster. Finally, AI can act as social catalysts to facilitate more effective collaboration within human teams or even help build hybrid teams of humans and machines working alongside one another…(More)”.

China wants tech companies to monetize data, but few are buying in

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Article by Lizzi C. Lee: “Chinese firms generate staggering amounts of data daily, from ride-hailing trips to online shopping transactions. A recent policy allowed Chinese companies to record data as assets on their balance sheets, the first such regulation in the world, paving the way for data to be traded in a marketplace and boost company valuations. 

But uptake has been slow. When China Unicom, one of the world’s largest mobile operators, reported its earnings recently, eagle-eyed accountants spotted that the company had listed 204 million yuan ($28 million) in data assets on its balance sheet. The state-owned operator was the first Chinese tech giant to take advantage of the Ministry of Finance’s new corporate data policy, which permits companies to classify data as inventory or intangible assets. 

“No other country is trying to do this on a national level. It could drive global standards of data management and accounting,” Ran Guo, an affiliated researcher at the Asia Society Policy Institute specializing in data governance in China, told Rest of World. 

In 2023 alone, China generated 32.85 zettabytes — more than 27% of the global total, according to a government survey. To put that in perspective, storing this volume on standard 1-terabyte hard drives would require more than 32 billion units….Tech companies that are data-rich are well-positioned tobenefit from logging data as assets to turn the formalized assets into tradable commodities, said Guo. But companies must first invest in secure storage and show that the data is legally obtained in order to meet strict government rules on data security. 

“This can be costly and complex,” he said. “Not all data qualifies as an asset, and companies must meet stringent requirements.” 

Even China Unicom, a state-owned enterprise, is likely complying with the new policy due to political pressure rather than economic incentive, said Guo, who conducted field research in China last year on the government push for data resource development. The telecom operator did not respond to a request for comment. 

Private technology companies in China, meanwhile, tend to be protective of their data. A Chinese government statement in 2022 pushed private enterprises to “open up their data.” But smaller firms could lack the resources to meet the stringent data storage and consumer protection standards, experts and Chinese tech company employees told Rest of World...(More)”.

Emerging Practices in Participatory AI Design in Public Sector Innovation

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Paper by Devansh Saxena, et al: “Local and federal agencies are rapidly adopting AI systems to augment or automate critical decisions, efficiently use resources, and improve public service delivery. AI systems are being used to support tasks associated with urban planning, security, surveillance, energy and critical infrastructure, and support decisions that directly affect citizens and their ability to access essential services. Local governments act as the governance tier closest to citizens and must play a critical role in upholding democratic values and building community trust especially as it relates to smart city initiatives that seek to transform public services through the adoption of AI. Community-centered and participatory approaches have been central for ensuring the appropriate adoption of technology; however, AI innovation introduces new challenges in this context because participatory AI design methods require more robust formulation and face higher standards for implementation in the public sector compared to the private sector. This requires us to reassess traditional methods used in this space as well as develop new resources and methods. This workshop will explore emerging practices in participatory algorithm design – or the use of public participation and community engagement – in the scoping, design, adoption, and implementation of public sector algorithms…(More)”.

The Data Innovation Toolkit

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Toolkit by Maria Claudia Bodino, Nathan da Silva Carvalho, Marcelo Cogo, Arianna Dafne Fini Storchi, and Stefaan Verhulst: “Despite the abundance of data, the excitement around AI, and the potential for transformative insights, many public administrations struggle to translate data into actionable strategies and innovations. 

Public servants working with data-related initiatives, need practical, easy-to-use resources designed to enhance the management of data innovation initiatives. 

In order to address these needs, the iLab of DG DIGIT from the European Commission is developing an initial set of practical tools designed to facilitate and enhance the implementation of data-driven initiatives. The main building blocks of the first version of the of the Digital Innovation Toolkit include: 

  1. A Repository of educational materials and resources on the latest data innovation approaches from public sector, academia, NGOs and think tanks 
  2. An initial set of practical resources, some examples: 
  3. Workshop Templates to offer structured formats for conducting productive workshops that foster collaboration, ideation, and problem-solving. 
  4. Checklists to ensure that all data journey aspects and steps are properly assessed. 
  5. Interactive Exercises to engage team members in hands-on activities that build skills and facilitate understanding of key concepts and methodologies. 
  6. Canvas Models to provide visual frameworks for planning and brainstorming….(More)”.

When forecasting and foresight meet data and innovation: toward a taxonomy of anticipatory methods for migration policy

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Paper by Sara Marcucci, Stefaan Verhulst and María Esther Cervantes: “The various global refugee and migration events of the last few years underscore the need for advancing anticipatory strategies in migration policy. The struggle to manage large inflows (or outflows) highlights the demand for proactive measures based on a sense of the future. Anticipatory methods, ranging from predictive models to foresight techniques, emerge as valuable tools for policymakers. These methods, now bolstered by advancements in technology and leveraging nontraditional data sources, can offer a pathway to develop more precise, responsive, and forward-thinking policies.

This paper seeks to map out the rapidly evolving domain of anticipatory methods in the realm of migration policy, capturing the trend toward integrating quantitative and qualitative methodologies and harnessing novel tools and data. It introduces a new taxonomy designed to organize these methods into three core categories: Experience-based, Exploration-based, and Expertise-based. This classification aims to guide policymakers in selecting the most suitable methods for specific contexts or questions, thereby enhancing migration policies…(More)”

Combine AI with citizen science to fight poverty

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Nature Editorial: “Of the myriad applications of artificial intelligence (AI), its use in humanitarian assistance is underappreciated. In 2020, during the COVID-19 pandemic, Togo’s government used AI tools to identify tens of thousands of households that needed money to buy food, as Nature reports in a News Feature this week. Typically, potential recipients of such payments would be identified when they apply for welfare schemes, or through household surveys of income and expenditure. But such surveys were not possible during the pandemic, and the authorities needed to find alternative means to help those in need. Researchers used machine learning to comb through satellite imagery of low-income areas and combined that knowledge with data from mobile-phone networks to find eligible recipients, who then received a regular payment through their phones. Using AI tools in this way was a game-changer for the country.Can AI help beat poverty? Researchers test ways to aid the poorest people

Now, with the pandemic over, researchers and policymakers are continuing to see how AI methods can be used in poverty alleviation. This needs comprehensive and accurate data on the state of poverty in households. For example, to be able to help individual families, authorities need to know about the quality of their housing, their children’s diets, their education and whether families’ basic health and medical needs are being met. This information is typically obtained from in-person surveys. However, researchers have seen a fall in response rates when collecting these data.

Missing data

Gathering survey-based data can be especially challenging in low- and middle-income countries (LMICs). In-person surveys are costly to do and often miss some of the most vulnerable, such as refugees, people living in informal housing or those who earn a living in the cash economy. Some people are reluctant to participate out of fear that there could be harmful consequences — deportation in the case of undocumented migrants, for instance. But unless their needs are identified, it is difficult to help them.Leveraging the collaborative power of AI and citizen science for sustainable development

Could AI offer a solution? The short answer is, yes, although with caveats. The Togo example shows how AI-informed approaches helped communities by combining knowledge of geographical areas of need with more-individual data from mobile phones. It’s a good example of how AI tools work well with granular, household-level data. Researchers are now homing in on a relatively untapped source for such information: data collected by citizen scientists, also known as community scientists. This idea deserves more attention and more funding.

Thanks to technologies such as smartphones, Wi-Fi and 4G, there has been an explosion of people in cities, towns and villages collecting, storing and analysing their own social and environmental data. In Ghana, for example, volunteer researchers are collecting data on marine litter along the coastline and contributing this knowledge to their country’s official statistics…(More)”.