At the Centre for Collective Intelligence Design, we focus on the practical skills and knowledge needed to bring humans and technology together in a way that enables groups to become smarter than the sum of their parts, and to mobilise human intelligence at greater scale. Find out about the experiments we are supporting in designing collective intelligence for social good.
In September 2018 we launched the first round of the Collective Intelligence Grants, offering up to £20,000 for experiments that advance knowledge on how to design and apply collective intelligence to solve social problems.
Find out about our twelve winning experiments from around the globe.
With society’s most pressing challenges becoming increasingly complex, opinions more and more polarised, and solutions more difficult to agree on, it is more important than ever to find new ways of thinking and acting together.
As the volume of data generated by crowds increases, so do the challenges of navigating and analysing it. This means that its full potential may go unrealised.
Four of our experiments will use machine learning to unlock new insights in large volumes of citizen-generated data. They will also explore how this changes its uptake and use to address social problems.
The success of collective intelligence initiatives often depends having an engaged crowd. How can we make sure that people are motivated to take part in collective intelligence initiatives, and what does it take to make their participation as effective as possible?
At the Centre for Collective Intelligence Design, we’ve been studying how collective intelligence is helping to address environmental issues, improve health and deliver better democracy. We believe collective intelligence has the potential to help solve many of the the most pressing issues we face today. Two of our experiments will test new use-cases for collective intelligence - in education and tackling urban food waste.
Over the next ten months, the people behind the experiments will be working on generating new insights into human-machine collaboration; shedding light on how the world looks to others; and on producing results that will be relevant and useful to collective intelligence practitioners and academics alike.