Towards Foundation-model-based Multiagent System to Accelerate AI for Social Impact
This addresses scalability and accessibility issues for social impact domain experts and AI researchers, though it appears incremental as it builds on existing foundation models.
The paper tackles the labor-intensive and resource-demanding nature of AI for social impact (AI4SI) research by proposing a novel meta-level multi-agent system to accelerate the development of base-level systems, aiming to reduce computational costs and expert burden.
AI for social impact (AI4SI) offers significant potential for addressing complex societal challenges in areas such as public health, agriculture, education, conservation, and public safety. However, existing AI4SI research is often labor-intensive and resource-demanding, limiting its accessibility and scalability; the standard approach is to design a (base-level) system tailored to a specific AI4SI problem. We propose the development of a novel meta-level multi-agent system designed to accelerate the development of such base-level systems, thereby reducing the computational cost and the burden on social impact domain experts and AI researchers. Leveraging advancements in foundation models and large language models, our proposed approach focuses on resource allocation problems providing help across the full AI4SI pipeline from problem formulation over solution design to impact evaluation. We highlight the ethical considerations and challenges inherent in deploying such systems and emphasize the importance of a human-in-the-loop approach to ensure the responsible and effective application of AI systems.