In September 2019, we launched the second round of the Collective Intelligence Grants in partnership with Wellcome Trust, Cloudera Foundation, and Omidyar Network. Together we created a £500,000 fund for experiments that could generate actionable insight on how to advance collective intelligence to solve social problems.
Find out how our second cohort of grantees is using new digital technologies and collective intelligence methods to address issues ranging from air pollution to food shortages, disease diagnosis and cyber violence.
Exploring AI-crowd interaction
At the Centre for Collective Intelligence Design, we believe that to tackle complex problems we need to mobilise all the resources of intelligence available to us. That’s why we are working to understand how to best combine the complementary strengths of machine intelligence and collective human intelligence.
Six of our grantees are experimenting with AI and crowd interaction.
- Reducing cyber bullying and harassment on the internet is an important part of building the web we want. As part of this effort, we will support Samurai Labs to explore how an AI cyber violence detection system and groups of people can work together to reduce the level of online harassment on social media site Reddit.
- Tackling complex problems requires new, creative and effective solutions. Online innovation platform Neu, with City, University of London will test whether a ‘serendipity-inducing’ recommendation algorithm, which increases the randomness of results in online search, can help teams of innovators to come up with more creative solutions to plastic waste and ageing societies.
- Improving diagnosis of health conditions is critical to identifying the right treatment for patients. ISTC-CNR, the Max Planck Institute for Human Development and the HumanDX Project will try to improve the accuracy of collective medical diagnosis by doctors on the HumanDX platform, using an AI tool called a knowledge graph.
- Humanitarian OpenStreetMap and The Red Cross Netherlands will explore the application of machine learning for detecting buildings and roads from satellite imagery in relatively unmapped regions in Africa and Asia. They will then test the extent to which this increases the speed and quality of community mapping initiatives - which is vital to enabling effective humanitarian response efforts.
- Spotlab will test whether it can build accurate AI models for the diagnosis of neglected tropical diseases such as Leishmania. It will do this by training AI models on digitised images of blood samples classified by crowds playing online games. It will then compare these with AI models trained on images classified by health professionals.
- The International Organisation for Migration will create a platform that brings together multiple data sets related to droughts, such as numbers and location of displaced people, rainfall and vegetation health. Their experiment will test whether involving affected communities in a collective analysis of AI-driven data insights from this platform will improve the effectiveness of the humanitarian response.
Better collective decisions - increasing the diversity of the people and opinions you listen to
As we become more polarised in our views, and challenged by the need to make rapid decisions on emerging issues, it can be hard to ensure that we listen to diverse perspectives. But research shows that embracing diversity of experience and opinions is key to better decisions and solving problems more effectively.
Three of our grantees are testing new methods for more inclusive collective decisions:
- Bristol University will test whether a swarm of 100 small robots interacting with people in a crowd can facilitate new social interactions and opinion sharing that can help groups to reach a consensus.
- The Centre for Cognition, Computation, and Modelling at Birkbeck University of London, will create an algorithm that manipulates the composition of groups online to maintain high opinion diversity within each group. The experiment will explore whether this algorithmically moderated social network will produce more accurate collective decisions than other groups by reducing the likelihood of groupthink.
- RadicalxChange Foundation and Oxfam Kenya will test quadratic voting (QV), a novel method for making collective decisions. The experiment will test whether QV can outperform traditional voting mechanisms in drawing out women’s priorities in a participatory budgeting exercise in Kenya.
Understanding the dynamics of collective behaviour
The importance of understanding collective behaviour in relation to disasters such as floods, and pandemics is obvious. It is also significant in tackling many of the complex challenges that we are grappling with in the 21st century, from rising obesity levels to living within planetary boundaries.
Three of our grantees will investigate how positive behaviour spreads and how collective behaviour change can be encouraged.
- IEIIT-CNR, together with Queen’s University and Ryerson University, will test whether positive deviance (people in a community who have uncommon but successful behaviours or strategies) and data-driven segmentation of patients for peer-learning groups can help patients with poor diabetes control learn from those ‘like them’ who are successfully managing their disease.
- Tackling emerging and environmental challenges will require people to cooperate in using resources (such as food or oil) responsibly and to coordinate in sharing information. The University of Nottingham, RMIT University and the University of Tasmania will run an experiment that tests how different levels of social connectivity within and between overlapping social groups on an online platform can improve coordination in response to collective crises.
- Umbrellium, Loop Labs & Tower Hamlets Council will work with local communities in the borough to generate geo-located sensor data and subjective ‘sense data’ of air quality. The experiment will test the impact of collective data generation, as well as the sharing and coordination of personal actions on sustaining behaviour change to reduce air pollution.
Collective intelligence for better data
Around the world there has been a data revolution driven by advances in information technology. But for many developing countries and many complex issues there are still data gaps. Collective intelligence approaches that involve people in generating or classifying data can help create more localised and real-time information, address bias in existing data sets, and audit or monitor AI systems.
- Drones for Humanity (Kenya Flying Labs) & Kenya Red Cross will run an experiment to test whether a public health surveillance system combining predictive analytics, crowdsourced local knowledge and aerial imagery can help reduce cholera outbreaks in peri-urban settings in Kenya.
- Clinical trials can be slow and expensive and they may not address the real-life needs of patients managing complex conditions. Just One Giant Lab and Open Humans Foundation aim to make it easier for patients to do research together on the questions that matter most to them. They will develop and test a platform that enables patients to find others in their community with the skills and interests to work together on shared research ideas - exploring, analysing and donating their data to answer important questions.
- Farmers without access to the internet or mobile phone coverage often don't have access to local weather information which limits their ability to adapt to extreme weather events or natural disasters. Swisscontact, the Banco de Desarrollo Productivo and the Latin American Centre for Rural Development will work with farmers in rural Bolivia to crowdsource weather data using low cost sensors. The experiment will test whether this locally crowdsourced weather information will enable farmers to better prepare for freezing temperatures, reducing crop loss.
- Dovetail Labs will involve the public in a citizen science project to explore the contested social science behind AI-based emotion recognition systems. These systems use algorithms to detect emotions like anger or happiness from facial images and their use is controversial. It will also measure the impact of the project on public understanding of biases in the data sets that the tech systems are built on.
During 2020, the teams behind these experiments will be working on generating new insights into how human-machine collaboration can help address our most complex social challenges. We will be sharing their results in 2021.
Read about the results of our first cohort of collective intelligence grantees.