Artificial Intelligence for Social Good (AI4SG) is an emerging effort that aims to address complex societal challenges with the powerful capabilities of AI systems. These challenges range from local issues with transit networks to global wildlife preservation. However, regardless of scale, a critical bottleneck for many AI4SG initiatives is the laborious process of problem scoping -- a complex and resource-intensive task -- due to a scarcity of professionals with both technical and domain expertise. Given the remarkable applications of large language models (LLM), we propose a Problem Scoping Agent (PSA) that uses an LLM to generate comprehensive project proposals grounded in scientific literature and real-world knowledge. We demonstrate that our PSA framework generates proposals comparable to those written by experts through a blind review and AI evaluations. Finally, we document the challenges of real-world problem scoping and note several areas for future work.

本研究解决了社会公益人工智能（AI4SG）项目中的问题界定过程效率低下的问题，特别是缺乏具备技术和领域专业知识的人才。我们提出了一种名为问题界定代理（PSA）的方法，该方法利用大型语言模型生成基于科学文献和现实知识的全面项目提案，结果表明，PSA生成的提案与专家撰写的提案对比相当，有助于推进AI4SG项目的实施。 

面向社会公益项目的人工智能自动化问题界定