Collective crisis intelligence (CCI) combines methods that gather information from communities affected by crises and frontline responders using artificial intelligence (AI) for more effective crisis mitigation, response and recovery.
Using CCI methods, the research project has prototyped tools in Nepal and Cameroon with local people and communities generating new insights into how CCI can improve and localise humanitarian action.
With humanitarian crises increasing in scale and frequency, new solutions and tools are needed to better predict and manage crises, and reduce their impact. By testing and developing models of participatory AI, new evidence and knowledge can be generated on how to use CCI to enhance humanitarian response.
The development of CCI solutions builds a richer and more local understanding of crises, drawing on the expertise of frontline responders and communities. Combined with the processing power of AI technologies, this gives humanitarians access to more timely and contextual data for anticipatory action, effective response and sustainable recovery.
Related reports and resources to this investment:
Collective Crisis Intelligence for Frontline Humanitarian ResponseThis report provides the first analysis of how an emerging innovation approach, Collective Crisis Intelligence, is being used to improve anticipation, management and response in the humanitarian sector. |
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Localising AI for Crisis ResponseThis report documents the results of a year-long project to design and evaluate new Collective Crisis Intelligence tools that combine data from crisis-affected communities with the processing power of AI to improve humanitarian action. |
Download |
Online course: Community Crisis Intelligence - Elevating community voices with AIThis course is designed for anyone in the humanitarian sector who wants to learn more about leveraging the potential of community intelligence and AI. |
Access course |
AI Safety Label explainerThis explainer shows how Nesta has been developing and testing a conceptual AI Safety Label as part of ongoing research to promote the responsible adoption of humanitarian AI tools. |
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Genna Barnett, Aleks Berditchevskia, Issy Gill, Nasara Hussein, Eirini Malliaraki, Joana Murta Rosa, Kathy Peach, George Richardson, Isabel Stewart, Oli Wittington; Fellows: Princewill Achem Nkongho, Pius Ngwa, Saurav Poudel
UK, Nepal and Cameroon
Collective crisis intelligence
Pius is a data scientist with an MSc in data science, and a background in teaching and software engineering.
He is interested in natural language processing and using AI for social good. As a UKHIH fellow, Pius helped develop an AI prototype to fight disinformation at community level, curating and processing data, and leading the unsupervised approach of the AI tool.
Collective crisis intelligence
Saurav is a data science teacher with experience in the health, humanitarian and social science domains.
As a UKHIH fellow, he worked on a project to create a machine learning model to optimise the distribution of relief items in crisis situations.
Citizen engagement and technology in crisis settings
Princewill is a development evaluation specialist with expertise in monitoring and evaluation, humanitarian relief, risk communication, health promotion and social media monitoring.
His experience spans WHO HQ, the IFRC Africa Regional Office, Catholic Relief Services, the Malaria Consortium and GIZ. As a UKHIH fellow, he worked with the Cameroon Red Cross Society to support humanitarian emergency response through using AI to improve community feedback mechanisms.