Collective Intelligence for Sustainable Development: Getting Smarter Together.
This report sets out how governments and the many organisations involved in global development are increasingly mobilising not just money but also intelligence to speed up progress towards the Sustainable Development goals.
From citizens' insights to grassroots solutions, data from mobile phone companies 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'.
This research, a collaboration between Nesta’s Centre for Collective Intelligence Design and the UNDP Accelerator Labs, is the first attempt to map how diverse collective intelligence approaches are being used to speed up progress on the Sustainable Development Goals (SDGs).
‘Collective Intelligence for Sustainable Development' analyses and compares the methods and tools used by over 200 organisations.This research presents six key ‘use cases’ - practical ways in which people are using collective intelligence approaches for the Sustainable Development Goals (SDGs). It also outlines the 15 key methods that are being used most frequently: from crowdsourcing to web scraping and remote sensing. And it shows how AI is being used to augment the collective intelligence of large groups of people.
The six use cases where collective intelligence is already making a difference to the SDGs include:
- New forms of accountability and governance: where eyewitness video and crowdmapping are being used to document violence or human rights abuses, with a view to holding perpetrators to account.
- Anticipating, monitoring and adapting to systemic risks: where a combination of web-scraped data, crowdsourced observations from volunteers, and location information from crowdmappers are helping organizations to improve their capacity for early warning and monitoring of, and response to, natural disasters, conflict and epidemics.
- Real-time monitoring of the environment: where citizen science and in-situ or remote sensing methods (such as satellites) complement existing ways of monitoring the state of environments – from air quality to deforestation – to fill data gaps in environmental monitoring.
- Understanding and working with complex systems: where collective intelligence is helping policy makers and development organizations to visualize the dynamics of complex systems, uncover insights that have previously been hidden and understand the different needs or experiences of diverse or changing populations.
- Inclusive development and technologies: where crowdmapping, citizen reporting and mobile phone surveys can be used to engage people whose voices are often not counted, helping to deliver on the SDGs’ promise to ‘leave no one behind’. This use case also looks at methods to develop fairer artificial intelligence (AI) systems.
- Distributed problem solving: where organisations are tapping into people’s problem solving capabilities to make progress on issues where there is a lack of established solutions and practices, or when new and locally-appropriate solutions are in high demand.
This report is published in conjunction with a report on how UNDP’s Accelerator Labs are harnessing collective intelligence: 13 stories from the UNDP Accelerator Labs.
Orchestrating and scaling collective intelligence for the SDGs
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 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.