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.

翻译：社会影响力人工智能（AI4SI）在应对公共卫生、农业、教育、环境保护和公共安全等领域的复杂社会挑战方面展现出巨大潜力。然而，现有的AI4SI研究通常劳动密集且资源需求高，限制了其可及性和可扩展性；标准方法是针对特定的AI4SI问题设计一个（基础层级的）系统。我们提出开发一种新颖的元层级多智能体系统，旨在加速此类基础层级系统的开发，从而降低计算成本并减轻社会影响力领域专家和人工智能研究人员的负担。利用基础模型和大语言模型的最新进展，我们提出的方法聚焦于资源分配问题，为AI4SI全流程（从问题定义、解决方案设计到影响力评估）提供支持。我们强调了部署此类系统所固有的伦理考量和挑战，并强调采用人在回路方法的重要性，以确保人工智能系统的负责任和有效应用。