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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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)”.

How tax data unlocks new insights for industrial policy

Curated on Posted on by Stefaan Verhulst

OECD article: “Value-added tax (VAT) is a consumption tax applied at each stage of the supply chain whenever value is added to goods or services. Businesses collect and remit VAT. The VAT data that are collected represent a breakthrough in studying production networks because they capture actual transactions between firms at an unprecedented level of detail. Unlike traditional business surveys or administrative data that might tell us about a firm’s size or industry, VAT records show us who does business with whom and for how much.

This data is particularly valuable because of its comprehensive coverage. In Estonia, for example, all VAT-registered businesses must report transactions above €1,000 per month, creating an almost complete picture of significant business relationships in the economy.

At least 15 countries now have such data available, including Belgium, Chile, Costa Rica, Estonia, and Italy. This growing availability creates opportunities for cross-country comparison and broader economic insights…(More)”.

On the Shoulders of Others: The Importance of Regulatory Learning in the Age of AI

Curated on Posted on by Stefaan Verhulst

Paper by Urs Gasser and Viktor Mayer-Schonberger: “…International harmonization of regulation is the right strategy when the appropriate regulatory ends and means are sufficiently clear to reap efficiencies of scale and scope. When this is not the case, a push for efficiency through uniformity is premature and may lead to a suboptimal regulatory lock-in: the establishment of a rule framework that is either inefficient in the use of its means to reach the intended goal, or furthers the wrong goal, or both.


A century ago, economist Joseph Schumpeter suggested that companies have two distinct strategies to achieve success. The first is to employ economies of scale and scope to lower their cost. It’s essentially a push for improved efficiency. The other strategy is to invent a new product (or production process) that may not, at least initially, be hugely efficient, but is nevertheless advantageous because demand for the new product is price inelastic. For Schumpeter this was the essence of innovation. But, as Schumpeter also argued, innovation is not a simple, linear, and predictable process. Often, it happens in fits and starts, and can’t be easily commandeered or engineered.


As innovation is hard to foresee and plan, the best way to facilitate it is to enable a wide variety of different approaches and solutions. Public policies in many countries to foster startups and entrepreneurship stems from this view. Take, for instance, the policy of regulatory sandboxing, i.e. the idea that for a limited time certain sectors should not or only lightly be regulated…(More)”.

The Preventative Shift: How can we embed prevention and achieve long term missions

Curated on Posted on by Stefaan Verhulst

Paper by Demos (UK): “Over the past two years Demos has been making the case for a fundamental shift in the purpose of government away from firefighting in public services towards preventing problems arriving. First, we set out the case for The Preventative State, to rebuild local, social and civic foundations; then, jointly with The Health Foundation, we made the case to change treasury rules to ringfence funding for prevention. By differentiating between everyday spending, and preventative spending, the government could measure what really matters.

There has been widespread support for this – but also concerns about both the feasibility of measuring preventative spending accurately and appropriately but also that ring-fencing alone may not lead to the desired improvements in outcomes and value for money.

In response we have developed two practical approaches, covered in two papers:

  • Our first paper, Counting What Matters, explores the challenge of measurement and makes a series of recommendations, including the passage of a “Public Investment Act”, to show how this could be appropriately achieved.
  • This second paper, The Preventative Shift, looks at how to shift the culture of public bodies to think ‘prevention first’ and target spending at activities which promise value for money and improve outcomes…(More)”.

Nonprofits, Stop Doing Needs Assessments.

Curated on Posted on by Stefaan Verhulst

Design for Social Impact: “Too many non-profits and funders still roll into communities with a clipboard and a mission to document everything “missing.”

Needs assessments have become a default tool for diagnosing deficits, reinforcing a saviour mentality where outsiders decide what’s broken and needs fixing.

I’ve sat in meetings where non-profits present lists of what communities lack:

  • “Youth don’t have leadership skills”
  • “Parents don’t value education”
  • “Grassroots organisations don’t have capacity”

The subtext? “They need us.”

And because funding is tied to these narratives of scarcity, organisations learn to describe themselves in the language of need rather than strength—because that’s what gets funded…Now, I’m not saying that organisations or funders should never ask people what their needs are. The key issue is how needs assessments are framed and used. Too often, they use extractive “data” collection methodologies and reinforce top-down, deficit-based narratives, where communities are defined primarily by what they lack rather than what they bring.

Starting with what’s already working (asset mapping) and then identifying what’s needed to strengthen and expand those assets is different from leading with gaps, which can frame communities as passive recipients rather than active problem-solvers.

Arguably, a balanced synergy between assessing needs and asset mapping can be powerful—so long as the process centres on community agency, self-determination, and long-term sustainability rather than diagnosing problems for external intervention.

Also, asset-based mapping to me does not mean that you swoop in with the same clipboard and demand people document their strengths…(More)”.