Executive Summary

This report sets out how governments and the many organizations involved in global development are increasingly mobilizing not just money, but also intelligence to speed up progress towards the Sustainable Development Goals (SDGs).

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From citizen experiences to innovative grassroots solutions, mobile phone companies’ data to satellite imagery and AI – new resources of intelligence are being harnessed by organizations around the world to understand complex problems, make better decisions and find new solutions.

We bring together this diverse set of practices under the label of ‘collective intelligence’. At its simplest, collective intelligence can be understood as the enhanced capacity that is created when people work together, often with the help of technology, to mobilize a wider range of information, ideas and insights. Some of the methods employed are old. But many are new and require new skills and mindsets to make the most of them.

This analysis is the first attempt to map and understand how collective intelligence approaches are being used to address the Sustainable Development Goals. It documents many examples, and captures patterns in their application and impact.

At its simplest, collective intelligence can be understood as the enhanced capacity that is created when people work together, often with the help of technology and insight.

From our research we find that there are six key clusters of ‘use cases’ – practical ways in which people are using collective intelligence approaches for development. 

We discovered 15 methods that are being used most frequently, and often in combination. These range from crowdsourcing to web scraping and remote sensing, and we found that AI is also increasingly being used in parallel, mainly to increase the speed and efficiency of data processing at scale.

What these collective intelligence methods have in common is the use of technology to mobilize, make sense of, or augment the observations, insights and ideas of large numbers of people. 

The majority of the case studies we analyzed align most closely with targets related to SDGs 10-16 towards equity, sustainable cities, climate action and responsible governance, but we found examples cutting across all aspects of Agenda 2030.

Collective Intelligence and the SDGs: Six Use cases

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1. New forms of governance and accountability 

In this use case methods such as eyewitness video and crowdmapping are being used to document violence or human rights abuses, with a view to holding perpetrators to account. This use case also sees how governments are crowdsourcing ideas and opinions from citizens during policy making, and how citizens are generating new forms of data to monitor policy implementation.
SDGs 16

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2. Anticipating, monitoring and adapting to systemic risks

A wide range of collective intelligence methods are helping organizations to improve their capacity for early warning and monitoring of, and response to, natural disasters, conflict and epidemics. These include working with on-the-ground volunteers to provide data about emerging issues, or with crowdmappers to capture location information for crisis preparedness. Others combine datasets, including web scraped social media data, for real-time public health surveillance, or ask large groups of people to forecast geopolitical events.
SDGs 3, 13, 16

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3. Real-time monitoring of the environment

Collective intelligence methods like citizen science and in-situ or remote sensing methods (such as satellites) have been gaining traction as complements to existing ways of monitoring the state of environments – from air quality to deforestation. Web scraping social media and citizen reporting tools are also being used to generate information on environmental hazards from people in affected areas. This use case has the potential to fill data gaps in environmental monitoring.
SDGs 11, 14, 15

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4. Understanding and working with complex systems

Collective intelligence approaches that combine multiple data sources are helping policy makers and development organizations to visualize the dynamics of complex systems and uncover insights that have previously been hidden. City leaders are also increasingly turning to crowdsourcing ideas and opinions of their constituents to understand the different needs or experiences of diverse or changing populations.
SDGs 10, 11, 12

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5. Inclusive development and technologies

The SDGs’ promise to ‘leave no one behind’ brings with it an imperative to involve marginalized communities in development initiatives. Collective intelligence methods like crowdmapping, citizen reporting and mobile phone surveys can be used to engage people whose voices are often not counted. Crowdsourcing data from under-represented groups to train machine learning models is another growing trend that is important for developing fairer artificial intelligence (AI) systems.
SDGs 5, 10

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6. Distributed problem solving

To tap into people’s problem solving capabilities, organizations are: crowdsourcing solutions; convening peer-to-peer crowdsourcing of knowledge and experience; using open source repositories to share solutions for others to adapt and use; and crowd labeling data to train machine learning models. These collective intelligence methods have broad application across the majority of the SDGs, but become especially relevant for targets such as climate action, where there might be a lack of established solutions and practices, or when new and locally-appropriate solutions are in high demand.
SDGs 2, 3, 13

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Orchestrating and scaling collective intelligence for the SDGs

As we embark on a ‘decade of action’ for Agenda 2030, most would agree that to achieve the global goals and avert climate catastrophe the world will need to mobilize power and money as never before. But to use power and money well it will also be vital that governments, organizations, and communities become skilled in mobilizing intelligence of all kinds – data, information and ideas. 

The big challenge for the next few years will be to orchestrate collective intelligence more strategically or at scale. We suggest the following priorities:

Help governments make better use of collective intelligenc

Help governments make better use of collective intelligence

Local communities are collecting and sharing data on an unprecedented scale, while civil society organizations and social movements are doing pioneering work. Yet many governments are unfamiliar with the new sources of data available.

Make open source the default

Open source software and data such as OpenStreetMap, Ushahidi, Consul, Landsat and Sentinel have accelerated distributed experimentation with collective intelligence by a wide range of organizations. These open infrastructures are critical for collective intelligence and are increasingly underpinning effective action on the SDGs.

Considerations of ethics and personal privacy must be taken seriously in the design of collective intelligence projects

Collective intelligence depends on the trust and goodwill of participants. Organizations must prioritize people and purpose over technology – and ensure their projects promote data empowerment, not data extraction.

Funders should support AI and collective intelligence experimentation testbeds in real-world settings

Many have been slow to appreciate the vital importance of linking AI to collective human intelligence. But there is great scope to combine them together and in many fields AI risks being ineffective if it’s not integrated with human intelligence. A related priority should be to build up centers of expertise, particularly in sub-Saharan Africa, to counter the concentration of data and AI expertise in mainly US firms.

Create a stronger evidence base around impact and support collaborative experimentation in a greater number of communities

The field will also develop faster with greater support for innovators to share information and knowledge.

A call to action

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Realizing the potential of collective intelligence for the SDGs will be a collective task, but will build on much that is already underway. This report sets out some key roles that existing institutions could play.

The Organisation for Economic Co-operation and Development (OECD) could help to establish the protocols and standards that will be needed to underpin the shared data and knowledge infrastructures that will allow collective intelligence to be orchestrated more strategically.

The development banks could make it standard for any investment plan to include a complementary strand on the organization of intelligence – including the orchestration of data, science and evidence, as well as grassroots insights and wisdom.

Universities could help build the skills and experience that graduates will need to work in a collectively intelligent way. Many are now using the ‘challenge based’ model where, alongside their degree, students work on practical problem solving in teams that draw on multiple disciplines as well as insights outside of higher education.

Development partners could help grow civil society’s capacity to mobilize collective intelligence – supporting the skills needed, as well as speeding up the development of new methods and tools.

The private sector should make it easier for development organizations and innovators to access data and cloud services for SDG-related work.

The key strategic challenge for the UN is how to better orchestrate a broad range of intelligence relevant to the SDGs – from science and data, to public policy evidence and emerging findings from experiments – to help innovators on the ground work more effectively.

Why Collective Intelligence?
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