In this area, we would like to see proposals to develop AI tools that help humans resolve major cooperation challenges. By virtue of their potentially greater ability to identify mutually beneficial agreements or to create novel institutional designs, for example, AI systems could have a huge positive impact via helping humans to cooperate.  

• Development of AI tools for improving collective decision-making in settings where the humans involved have conflicting interests. This includes the use of AI tools for policy development.
• Development of AI tools to improve the collective outcomes of negotiation, bargaining and conflict resolution processes.
• Development of AI tools to design socially beneficial institutions or public services for enabling cooperation.

• For the proposal to be in scope, the AI system must be used to directly address the mixed-motive dynamics of the problem. This is an area where we receive many out-of-scope applications, so we encourage potential applicants for this area to read the scope description thoroughly and make sure they understand what is meant by cooperation problems in this context. Most uses of machine learning tools will not be in scope for our grant call (e.g. text summarization, image analysis, general data analysis, and most uses of interactive chatbot tools).
• Note that this area is focused on how AI can facilitate cooperation among groups of humans. We do not expect to fund work on facilitating cooperation between individual humans and AI systems. 
• The proposal should make a good case for how the work would contribute to improved resolution of important, high-stakes cooperation problems. Examples of domains that we count as high-stakes are:
  • Climate change
  • Armed conflict
  • Large-scale democratic systems and policymaking
  • Institutional design for a society with widespread AI agent deployment
• We do not expect to fund any work on social media dynamics, self-driving cars, improving market efficiencies or the efficiency of organisations/companies in general. The exception would be if any of those domains were used as a test domain to draw general conclusions that would be expected to apply to other important domains as well. We also receive a lot of out-of-scope applications for using AI to improve healthcare and agriculture. There could potentially be high-stakes cooperation problems in these domains that could be better solved using AI tools, but we particularly encourage applicants in these areas to review the scope description carefully.
• We are interested in funding both early work exploring the possibility of AI facilitation in the different areas, and concrete implementations of such tools. We prioritise funding work that we believe is unlikely to happen without our support. This means that projects with a significant commercial value, or which could otherwise be expected to be developed by the private sector, are very unlikely to be funded by us.
• Proposals should be explicit about the assumptions and definitions underlying what counts as a beneficial outcome. With a focus on how AI can support humans to improve the resolution of cooperation challenges, it will often be helpful if outcomes can be compared to human baseline performance.
• If the work targets a specific application domain (e.g. climate change), we strongly encourage the involvement of experts from that domain (and intended end users where applicable) in the development of the proposal to ensure real-world relevance.

• AI can Help Humans Find Common Ground in Democratic Deliberation
• Scaffolding Cooperation in Human Groups with Deep Reinforcement Learning
• Opportunities and Risks of LLMs for Scalable Deliberation with Polis
• Human-Centred Mechanism Design with Democratic AI
• The AI Economist
• Delegation to Artificial Agents Fosters Prosocial Behaviors in the Collective Risk dilemma
• Fair Algorithms for Selecting Citizens’ Assemblies