Understanding how AI can help us enhance collective human efforts to solve complex problems is at the heart of Nesta’s vision for a public interest AI. AI is increasingly being used within all fields and collective intelligence is no exception. Earlier this year, our report on the Future of Minds & Machines first drew attention to the need for more imaginative approaches to combining AI & collective intelligence (CI) in practice. By mapping case studies of collective intelligence in action, we found that most projects applied a fairly narrow range of AI methods to make sense of vast amounts of passively generated or actively crowdsourced user content. Almost all of these methods rely on big datasets and use machine-learning to find structure and patterns in “messy” data.

Far fewer projects used more novel AI approaches: this was true in terms of both methods and the kinds of tasks AI was being used for. For example, hardly any projects innovated with less popular AI techniques such as distributed AI, autonomous systems and evolutionary methods. And despite notable exceptions like the citizen science platform Zooniverse, there were few that applied AI in novel ways, for example, to improve the ways that information and ideas were shared or to enhance the collective output of the group during problem solving.

In order to better understand whether the barriers to imaginative combinations of AI and collective intelligence can be explained by the underlying research pipeline, we undertook a mapping and analysis of the academic literature in AI, CI and the crossover between them. We looked at three categories of publications:

CI: all non-AI collective intelligence literature,

AI: all non-CI literature on artificial intelligence,

AI+CI: the intersection between AI and CI.

We looked at how these fields have evolved since 2000 to shed light on the dynamics of the CI ecosystem and identify the trends and opportunities for the future to help researchers, practitioners, and funders make better decisions to help advance the field. We also call on the politicians and civil servants involved in setting AI industrial policy to take note of these trends.

Publications at the crossover of AI and collective intelligence are growing fast

Even though the total number of articles on the topic of AI in the last 20 years dwarfs the other 2 fields, the rate of increase in publications for CI and AI+CI has been significantly faster. Both showed a roughly similar rate of increase between 2008 and 2015 before diverging. In the last 5 years, AI+CI publications have continued to grow at a faster pace while rates of CI publications plateaued and even slightly declined.

• What does this mean? both the broader CI research domain and the more specialised AI+CI are rapidly growing fields.

The US, UK and China dominate the field

The main contributor to CI and AI+CI crossover research has consistently been the US (accounting for almost a third of the publications in our sample, 27%), with the UK in comfortable second place (17%) but in recent years research output from China has been gaining ground. Since 2014 total number of publications from the US and UK have remained more or less consistent while China’s research output in both CI and AI+CI has tripled over the same timeframe. This increase in publications from China coincides with the country’s growing dominance in AI: China produced 17% of the AI publications in our sample, compared with 19% and 13% from the US and UK, respectively.

• What does this mean? The US and UK stand to lose their advantage and the ability to determine the narrative. Less than a month ago, the Chinese government announced a major new fund to support AI technology that leverages user-generated content and collective intelligence, suggesting that this trend is likely to increase.

Industry is the key driver for AI+CI research

AI+CI publications from industry are growing at a much faster rate than academia. This stands in contrast with publications on either AI or CI alone, which have shown a similar rate of increase from researchers based in companies versus academia. Microsoft is responsible for the highest share of AI+CI publications amongst big tech companies. In general, AI+CI research tends to have a higher proportion of cross industry-academia collaboration, (between 10-15% since 2013) of all published papers than either AI or CI alone, although this has fallen to below 10% in the last 2 years.

• What does this mean? The constraints and incentives that determine the trajectory of research in industry are different to those in academia or the public sector. The dominance of industry-driven research for AI+CI may cause the overrepresentation of certain methods such as deep learning which are problematic in public-sector contexts where interpretability is important. This trend may also be limiting the kinds of questions we address with the combination of human and machine intelligence.

CI research shows more disciplinary breadth than AI+CI

Over the last 20 years, CI research has maintained high disciplinary breadth, with publications spanning Political Science, Engineering, Sociology, Computer Science and more. In contrast, AI+CI publications tend to fall within Computer Science, with a small proportion in Mathematics and Engineering. AI+CI research has become slightly more cross-disciplinary since 2000, perhaps reflecting the influence of CI. Even more striking, is the increased significance of Computer Science in “pure” CI research (more than 30% since 2004), with a commensurate reduction in publications from Sociology, Political Science and other fields. Readers can track these changes using the interactive chart below.

• What does this mean? Although it is unsurprising that CI publications from Computer Science have grown in the last 20 years the parallel decrease in the representation of other disciplines for pure CI research may be a worrying trend. One of the core tenets of collective intelligence design is diversity and in the past, the field has bridged across schools of thought as different as Biological Sciences and Business & Management. An increasingly narrow disciplinary focus may reduce the diversity of the research questions and the methods that are developed in the field. AI+CI stands to gain from the disciplinary breadth of CI research. The strong contribution to CI from socially relevant fields like Sociology and Political Science could be particularly useful for developing AI+CI methods and uses that prioritise collective benefit and public interest.

AI focused CI research has shifted towards deep learning

Since 2008, there has been a noticeable shift towards three topics growing in prominence in AI+CI research, namely ‘crowdsourcing’, ‘machine learning’ and, since 2015, ‘deep learning’. Even “pure” CI research has experienced a striking increase in ‘crowdsourcing’ publications in comparison to other topics. Only ‘citizen science’ has shown a similar rise in proportion of CI publications. Overall, AI+CI may be shifting away from a more integrated human-machine interaction (as evidenced by the fall in popularity of terms “social computing’ and ‘human computation’) towards the more transactional relationship to human labour demanded by supervised machine learning and deep learning algorithms. We invite readers to explore the changes in popular topics using the interactive charts below.

• What does this mean? Although CI publications have maintained a degree of topic diversity over the years, with publications covering a range of subjects from collaborative learning to citizen science and democracy, the increased focus on crowdsourcing, machine learning and deep learning in the AI+CI publications may pose a risk to this diversity in the future. Furthermore, if the most we can imagine about AI+CI research is to merely support the improvement of existing deep learning algorithms, we risk losing both the technological diversity in AI R&D and the societal focus that broader integration of CI topics into AI+CI could enable.

There are few opportunities for cross-fertilisation between AI and CI

Our analysis revealed that the top 20 publication venues (both conferences and journals) for the fields of AI and CI are largely non-overlapping. Only one conference and one journal featured in the top 20 for all three categories (AAAI and IEEE Access, respectively) and the only other touchpoint between the AI and CI categories was the bioRxiv repository. Looking more closely at the crossover of AI and collective intelligence (AI+CI) it becomes apparent that there is a higher degree of overlap in both journals and conferences between AI and AI+CI than with CI. This is unsurprising given the focus on technical topics and narrow disciplinary range revealed by our analysis above. AI+CI publications are thus more likely to be influenced by trends in AI research rather than CI. Although all of our categories had publications in arXiv in their top 20, AI and AI+CI publications were more likely to share subject labels than CI and AI+CI (e.g. arXiv computation and language, arXiv learning as the two with highest number of publications) This may influence co-discoverability of papers on the platform and the “light” peer review process used by aRxiv which affects the likelihood of researchers becoming aware of each others work.

• What does this mean? Conferences provide an important platform for the research community to find collaborators and share their work. Likewise journals and (increasingly) preprint repositories are leveraged by scientists to share ideas, results and data. If these important channels for cross-fertilisation are less established for researchers working in AI and CI, then it is unsurprising that the crossover publications are somewhat narrow.

Research funders and those determining industrial policy, particularly sector specific policy for AI in the UK should take note.

Our analysis reveals the need for:

• The UK to act decisively to ensure that China and the US don't end up setting the agenda for AI and collective intelligence in the future. Earlier this year, we recommended this takes the form of dedicated funding programmes and practical test-beds to better understand how these ideas operate in the real world.
• Active measures to ensure the disciplinary breadth of CI research is maintained, even as it benefits from the opportunities opened up by AI. As AI and Computer Science become increasingly integrated into our social systems, we will need the expertise of social scientists, psychologists, management scholars more than ever. Collective intelligence has a historical breadth of disciplinary focus which currently risks being lost unless cross-disciplinary collaboration is encouraged by funders and publishers.
• More imagination for the uses and methods tested at the crossover of AI and collective intelligence. We encourage researchers to look to and involve practitioners, those working in arts & culture and citizens in exploring more creative visions for the future of AI+CI, not only crowdsourcing and deep learning.
• More opportunities for the researchers working in AI and collective intelligence to cross-fertilise and share ideas. Conferences, workshops, summer schools and journals are typical instruments used by the research community to inspire sharing of ideas. Funding calls can also be important for stimulating collaboration. The recent citizen science collaboration fund launched by UKRI and our own Collective Intelligence Grants help to encourage more interdisciplinary thinking and social impact in the design of projects, but more is needed.

Unless we incentivise more imaginative uses of AI, ones that help us make the most of distributed human intelligence, we may end up thwarting the opportunities opened up by digital technologies and smart machines. Collective human intelligence and AI are intimately connected and mutually dependent, already most AI that we encounter in our everyday lives relies on collective human labour...some of it entirely invisible. By embedding CI principles into all stages of the AI development pipeline and drawing on the insights from Sociology, Psychology, Political Science and others to inform the ways we integrate AI into our society, we are more likely to avoid was has been called ‘the wrong kind of AI’ and ensure that the technology is maximised for the public interest.