Artificial Intelligence for Social Good (AI4SG) is a growing area that explores AI's potential to address social issues, such as public health. Yet prior work has shown limited evidence of its tangible benefits for intended communities, and projects frequently face real-world deployment and sustainability challenges. While existing HCI literature on AI4SG initiatives primarily focuses on the mechanisms of funded projects and their outcomes, much less attention has been given to the upstream funding agendas that influence project approaches. In this work, we conducted a reflexive thematic analysis of 35 funding documents, representing about $410 million USD in total investments. We uncovered a spectrum of conceptual framings of AI4SG and the approaches that funding rhetoric promoted: from biasing towards technology capacities (more techno-centric) to emphasizing contextual understanding of the social problems at hand alongside technology capacities (more balanced). Drawing on our findings on how funding documents construct AI4SG, we offer recommendations for funders to embed more balanced approaches in future funding call designs. We further discuss implications for how the HCI community can positively shape AI4SG funding design processes.

翻译：人工智能向善（AI4SG）是一个日益发展的领域，旨在探索人工智能解决公共卫生等社会问题的潜力。然而，先前研究表明，其为目标社区带来的切实效益证据有限，且项目常常面临现实世界部署和可持续性方面的挑战。尽管现有关于AI4SG倡议的人机交互文献主要关注资助项目的机制及其成果，但对影响项目方法的上游资助议程却关注甚少。本研究对35份资助文件进行了反思性主题分析，这些文件代表了总计约4.1亿美元的投资。我们揭示了AI4SG的一系列概念框架以及资助话语所倡导的方法：从偏向技术能力（更以技术为中心）到在关注技术能力的同时强调对相关社会问题的情境理解（更均衡）。基于资助文件如何构建AI4SG的发现，我们为资助方提出建议，以在未来资助项目设计中嵌入更均衡的方法。我们进一步讨论了人机交互社区如何积极影响AI4SG资助设计过程的启示。