NSF Award
2303000
This project aims to develop (artificial intelligence) AI-powered approaches to address challenging societal problems, such as dealing with droughts, infectious diseases, and environmentally harmful emissions. This project engages specific questions in these areas, such as: How can one effectively allocate water resources to increase agricultural drought resilience during drought seasons? How can one effectively determine when and where to construct hydrogen or electric vehicle refueling stations to encourage citizens to adopt these technologies to lower emissions? How can one effectively distribute vaccines and other medical supplies daily to enhance response to infectious diseases during pandemics? These problems belong to a class of classical and important problems in sequential collective decision-making. While sequential decision-making and collective decision-making have been studied previously, decision-making problems that are simultaneously sequential and collective are poorly understood, especially for specific domains such as resource allocation and when combined with goals such as responsibility and equitability. The overarching project goal is to establish theoretical and algorithmic foundations for responsible and equitable AI-powered sequential, collective decision-making. It also seeks to ensure that sequences of decisions satisfy multiple objectives and make appropriate trade-offs between short and long-term rewards subject to fairness criteria. The proposed research will lead to efficient and fair solutions to our social good and use-inspired applications in drought resilience, towards net zero, and infectious disease resilience. <br/><br/>The project will achieve its goals by focusing on three interconnected challenges, leveraging a wide range of techniques from AI, economics, and operation research. First, the challenge of fair multi-objective collective decision-making for a single time period subject to multiple objectives and fairness criteria. Second, explore fair sequential multi-objective collective decision-making that addresses trade-offs between immediate and long-term efficiency and fairness. Finally, understand the strategic aspects of fair multi-objective collective decision-making in collaboration with stakeholders, who provide information that facilitates the process.<br/><br/>This is a joint project between United States and Australian researchers funded by the Collaboration Opportunities in Responsible and Equitable AI under the U.S. NSF and the Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO).<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
