In The future of minds and machines, we mapped more than 100 case studies that made use of both AI and collective intelligence (CI) in sectors from health to agriculture and urban planning. We analysed 50 of these in detail in order to better understand the opportunities offered by combining human and machine intelligence for solving social challenges. Even though our analysis revealed that AI is already being applied across CI contexts, ranging from citizen science to crowd predictions (see the figure above and Making the most of the CI opportunity for more details), we found that there was not much variety or imagination in the ways that AI is used.

A large proportion of projects relied on either Computer Vision or Natural Language Processing which use machine-learning to process vast amounts of data, text and images. Given that many CI projects collect citizen-generated images, videos and text from participants, it is unsurprising that these AI methods dominated our sample. After all, a large amount of unstructured data is an ideal match for a data-hungry machine-learning algorithm. Current integration of AI is, therefore, predominantly aimed at the "low-hanging fruit" of overcoming data challenges faced by CI, leading to gains in efficiency and scale.

However, new investment and public sector experimentation could help to encourage methodological innovation in AI and CI over the next 5–10 years. In the future, we hope to see more imaginative uses of AI for collective benefit. This might come from improvements to existing technology or the transfer of current methods to new contexts. We discuss some of the potential sources of innovation below.

A group of methods referred to as distributed artificial intelligence (DAI)[1] have been gaining attention in recent years. Like many AI techniques, DAI methods are not new but using them in combination with machine-learning has resulted in new opportunities for better models of complex systems.

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DAI is a class of algorithms based on the activities of many individual autonomous agents, each of which generates solutions to small parts of the overall problem. DAI is inspired by collective behaviour in the natural world, such as the swarming of bees or the interaction of individuals in social networks, and is often used to model complex systems with many parts. What all DAI approaches have in common is that no individual agent has enough information to get to the overall solution by itself.

DAI models are still more frequent in research settings. For example, conservation and ecology researchers have used DAI to explore the management of forests, the timing of animal migrations, and the population pressures on endangered species but there is increasing interest in using them to visualise the complexity of systemic public sector problems in scenarios ranging from urban waste management and forecasting the spread of infectious disease to the co‑ordination of emergency responses.

[1] Agent-based modelling (ABM), Multi-agent systems (MAS) and Swarm intelligence (SI) are different methodological approaches to DAI.

There is also a growing emphasis on deploying AI methods in combination with other technologies, including blockchain, quantum computing and the Internet of Things. Most of these convergence technologies are still being tested in research and development contexts and are far from being applied to high-stake problems in the real world.

Many of the newest methodological advances are less studied and so introducing them into the public sector setting too early poses a risk. The creation of controlled, smaller-scale testbeds for public problem‑solving could help to mitigate this risk while allowing the public sector and civil society to benefit from the latest innovations in the field.

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