AI for European grand challenges

AI and the EU’s Innovation Missions

As countries across Europe seek to recover from the impacts of COVID-19, policymakers face a constellation of challenges; the need to recover economic growth, to ease pressure on healthcare systems and improve wellbeing, to manage disruption to energy systems, to build resilience to environmental change, and more. With public services in many areas under increasing strain, there is a pressing need to translate policy aspirations for AI to action.

The EU’s Innovation Missions offer a vehicle to understand the connections between areas of critical societal need and AI’s technical capabilities, and to chart a path for AI that helps address these needs. These five Missions articulate policy priorities and set out a framework for research and innovation to deliver solutions to these challenges. By 2030, their ambition is to:

Support at least 150 European regions and communities to become climate resilient

Improve the lives of more than 3 million people through cancer prevention and treatment

Restore European oceans and waters

Create 100 climate-neutral and smart European cities

Lead a transition towards healthy soils through 100 living labs and lighthouses

Connecting AI to policy priorities

The potential of AI stems from its pervasiveness: a variety of AI tools now exist that can be adapted to help tackle areas of pressing need across different application domains. The illustrations above of how AI can be deployed to help deliver the Innovation Missions are diverse in their purpose, focus, and the disciplinary expertise they engage. Together, they point to a pipeline of AI interventions for mission delivery.

AI provides an adaptable tool to:

  • Monitor and make sense of the complex systems that influence human wellbeing.
  • Predict changes or events in those systems for which preparatory action is needed.
  • Enhance human decision-making, leveraging insights from data to create strategies for action.

The demands that these applications of AI in priority policy areas make of AI technologies share similar characteristics. They require sophisticated technical methods that can analyse complex datasets to deliver actionable insights; they need AI methods that can be deployed into real-world systems; and they demand AI technologies that work effectively alongside people, aligning with the needs, interests, and concerns of human users.

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Advanced analytical techniques

Each of the Mission domains represents a complex system. Climate adaptation and ecosystem management require an understanding of the climate system, and the interactions between physical, biological, and human processes that influence the behaviour of its sub-systems over space and time. Cities are built from complex interconnections between people, technology, and infrastructure. The emergence and progress of cancer in different patients are influenced by complex genetic, economic, lifestyle, and social factors. These systems are characterised by interacting physical, environmental, social, and technological influences across different scales, resulting in unpredictable or emergent properties, where the localised or individual impacts of policy interventions are hard to predict, and where the long-term impact of those interventions are difficult to anticipate.

In response, advanced analytical tools are needed that can work with real-world data, extracting insights from that data and combining those insights with existing domain knowledge, allowing users to interrogate the workings of complex systems. Each of these needs represents active areas of research.

Analyse real-world data
Bridge between data-driven and domain knowledge
Interrogate workings of complex systems

Deployable methods and tools

These advanced analytical tools need to be deployable into real-world environments, which are often dynamic or uncertain and where their interactions with human users influence their effectiveness. Despite much progress in the capabilities of AI, many attempts to deploy these technologies in complex, real-world systems fail. A recent review of the use of AI in COVID-19 diagnosis, for example, highlighted that many AI systems developed for use in pandemic response were unsuitable for deployment.  More robust AI methods are needed to tackle this ‘deployability’ issue; such methods should be safe and effective in real-world systems, which are typically characterised by changing conditions, unexpected events, and complex incentive structures. To serve human users, AI techniques also need to function as decision-support tools, requiring effective communication and collaboration with human users.

Robust under dynamic conditions
Effective as decision-support
Explainable and interpretable in decision-making

AI that works for people

Who benefits from advances in AI, and who bears the risks associated with its use, is shaped by a range of digital divides. How to manage the development and deployment of AI without reinforcing patterns of exclusion – enabling all in society to benefit from its use – remains a major challenge for researchers, practitioners, and policymakers. The EU’s AI policy frameworks highlight the importance of ensuring that AI reflects the rights and values set out in European law, through ethical principles to guide its development and use, alongside regulatory proposals to govern high-risk applications. Human-centric AI seeks to embed these ideas in research and development. Applications in the context of the Innovation Missions highlight the importance of AI that demonstrates trustworthiness, that is integrated effectively into decision-making, and that represents diverse perspectives and needs.

Integrated into practice
Represent diverse perspectives

An AI for Grand Challenges policy agenda

The technical agenda that emerges from this review of the application of AI to deliver the Innovation Missions highlights the importance of developing AI systems that are technically advanced, robust in deployment, and aligned with societal interests. Alongside this agenda for AI technology emerges a policy agenda to accelerate the use of AI in areas of critical societal need.

To support further progress in the deployment of AI to address the goals of the Innovation Missions, policy interventions are needed to:

Advance the technical foundations and deployability of AI in real-world systems
Accelerate end-to-end innovation through challenge-led programmes and pathfinder projects in areas of need
Connect to local innovation ecosystems and citizen interests
Build an infrastructure for AI R&D
Attract talent and build skills
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An evolving research-policy agenda