Collective crisis intelligence for frontline humanitarian response
This report provides the first analysis of how an emerging innovation approach, ‘collective crisis intelligence’ (CCI), is being used to improve anticipation, management and response in the humanitarian sector. CCI combines methods that gather on-the-ground human intelligence from crisis-affected communities and frontline responders with artificial intelligence (AI).
This report is for the wider community of humanitarian innovators and innovation funders, whom we hope will use the findings to spur further R&D efforts in this emerging area.
This report is published in conjunction with a paper on Participatory AI, which maps out current approaches to participatory design of AI systems and explores how these can be adapted to humanitarian settings: participatory AI for humanitarian innovation: a briefing paper
‘Collective crisis intelligence for frontline humanitarian response' is the first ever landscape analysis of CCI solutions in the humanitarian sector. This research presents a technical analysis of existing solutions looking at a range of dimensions (i.e. data sources and technology types), identifies five categories of existing and emerging ‘use cases’ for CCI, and outlines the 10 key R&D opportunities for future investment. We invite you to explore 5 inspiring examples of CCI solutions here.
Four key findings from our analysis are:
1. Collective crisis intelligence is predominantly used for early warning of crises and real-time information for effective response.
The vast majority (68%) of CCI solutions are being used to provide early warning of a crisis, or real-time situational information during the preparedness and response phases of crisis management. Almost half of all cases are focused on rapid-onset natural disasters, such as floods, earthquakes and hurricanes.
2. Collective crisis intelligence is a nascent field.
Many of the CCI solutions analysed were at an early stage of development, with a significant proportion in concept/idea or prototype stages. The early stage of development means that few have been integrated into humanitarian workflows or systems. Better integration is needed and this will require work to overcome organisational and technical barriers, such as a lack of leadership buy-in and the digital skills gap.
3. Collective crisis intelligence could help strengthen localisation, anticipatory action and a more human-centred AI.
By drawing on novel data sources, including from responders and communities on the frontline, CCI solutions can build a richer local and social understanding of crises. Combining these with the processing power of AI technologies means humanitarians can have access to more timely and contextual data - which can be used for anticipatory action, effective response or sustainable recovery.
4. Emerging applications of collective crisis intelligence include modelling of interventions for more effective programme planning.
CCI solutions could better support longer-term planning and decision making for mitigation or recovery efforts through modelling interventions during programme planning. For example, modelling processes can enable different stakeholder groups to gain a collective understanding of impacts, dependencies, and emergent or unintended effects. In particular, participatory modelling approaches have the potential to enhance collaboration and build trust between stakeholder groups.
Ten R&D opportunities for collective crisis intelligence. Our research highlighted three important categories future investment:
Expanding CCI solutions to new users
- Develop CCI solutions that both draws on expertise of frontline workers, and enables them to take action
- Use collective intelligence methods to deepen community participation through active (rather than passive) contributions, such as crowdsourcing ideas
Applying CCI solutions to new issues in crisis management
- Expand situational awareness of misinformation and disinformation through CCI solutions
- Predict the resources needed for crisis mitigation, response and recovery
- Involve communities in real-time monitoring and evaluation of humanitarian response or recovery efforts
- Leverage CCI for distributed intelligent actions that involves both nontraditional and traditional actors
Leveraging new technologies in CCI solutions
- Leverage unsupervised or semi-supervised machine learning techniques for improved situational awareness
- Model the complexity of crises and the effects of humanitarian challenges and actions
- Use participatory modelling for improved multi-stakeholder decision making
- Use CI to bridge the gap between human reasoning and AI predictions
This research is part of a larger project on CCI for humanitarian action that Nesta is delivering in partnership with IFRC Solferino Academy. It is funded by a grant from the UK Humanitarian Innovation Hub (UKHIH), which is funded by the UK’s Foreign, Commonwealth and Development Office (FCDO) and hosted by Elrha - a global humanitarian organisation and the UK’s leading independent supporter of humanitarian innovation and research.