Blog by Stefaan Verhulst: “As the technology, research and policy communities continue to seek new ways to improve governance and solve public problems, two new types of assets are occupying increasing importance: data and people. Leveraging data and people’s expertise in new ways offers a path forward for smarter decisions, more innovative policymaking, and more accountability in governance. Yet, unlocking the value of these two assets not only requires increased availability and accessibility (through, for instance, open data or open innovation), it also requires innovation in methodology and technology.
The first of these innovations involves Artificial Intelligence (AI). AI offers unprecedented abilities to quickly process vast quantities of data that can provide data-driven insights to address public needs. This is the role it has for example played in New York City, where FireCast, leverages data from across the city government to help the Fire Department identify buildings with the highest fire risks. AI is also considered to improve education, urban transportation, humanitarian aid and combat corruption, among other sectors and challenges.
The second area is Collective Intelligence (CI). Although it receives less attention than AI, CI offers similar potential breakthroughs in changing how we govern, primarily by creating a means for tapping into the “wisdom of the crowd” and allowing groups to create better solutions than even the smartest experts working in isolation could ever hope to achieve. For example, in several countries patients’ groups are coming together to create new knowledge and health treatments based on their experiences and accumulated expertise. Similarly, scientists are engaging citizens in new ways to tap into their expertise or skills, generating citizen science – ranging from mapping our solar system to manipulating enzyme models in a game-like fashion.
Neither AI nor CI offer panaceas for all our ills; they each pose certain challenges, and even risks. The effectiveness and accuracy of AI relies substantially on the quality of the underlying data as well as the human-designed algorithms used to analyse that data. Among other challenges, it is becoming increasingly clear how biases against minorities and other vulnerable populations can be built into these algorithms. For instance, some AI-driven platforms for predicting criminal recidivism significantly over-estimate the likelihood that black defendants will commit additional crimes in comparison to white counterparts. (for more examples, see our reading list on algorithmic scrutiny).
In theory, CI avoids some of the risks of bias and exclusion because it is specifically designed to bring more voices into a conversation. But ensuring that that multiplicity of voices adds value, not just noise, can be an operational and ethical challenge. As it stands, identifying the signal in the noise in CI initiatives can be time-consuming and resource intensive, especially for smaller organizations or groups lacking resources or technical skills.
Despite these challenges, however, there exists a significant degree of optimism surrounding both these new approaches to problem solving. Some of this is hype, but some of it is merited—CI and AI do offer very real potential, and the task facing both policymakers, practitioners and researchers is to find ways of harnessing that potential in a way that maximizes benefits while limiting possible harms.
In what follows, I argue that the solution to the challenge described above may involve a greater interaction between AI and CI. These two areas of innovation have largely evolved and been researched separately until now. However, I believe that there is substantial scope for integration, and mutual reinforcement. It is when harnessed together, as complementary methods and approaches, that AI and CI can bring the full weight of technological progress and modern data analytics to bear on our most complex, pressing problems.
To deconstruct that statement, I propose three premises (and subsequent set of research questions) toward establishing a necessary research agenda on the intersection of AI and CI that can build more inclusive and effective approaches to governance innovation.
Premise I: Toward Augmented Collective Intelligence: AI will enable CI to scale
Premise II: Toward Human-Driven Artificial Intelligence: CI will humanize AI
Premise III: Open Governance will drive a blurring between AI and CI
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