Agentic AI systems are increasingly proposed for social-good domains, often invoking the United Nations Sustainable Development Goals (SDGs) as a vocabulary of global benefit. Yet claims of social good do not establish accountability to the communities a system claims to serve. We present a structured survey of 112 papers on agentic AI for social good published between 2015 and 2026. We find a moral-geographic asymmetry: papers are least likely to specify geographic context in precisely the domains where local political, legal, and cultural context matters most. Across the corpus, 82 of 112 papers (73%) specify no geographic context. Papers aligned with health or physical/ecological SDGs specify geography 37-40% of the time, while papers aligned with institutional and social-policy SDGs do so only 13%. SDG 16, peace, justice, and strong institutions, is both the most-covered goal in the corpus and the one with the lowest geographic-specification rate. We interpret this as moral abstraction: agentic AI for social good often treats institutional good as universal in ways it does not treat health or ecological good. A second finding compounds this: only 28 of 112 papers (25%) report any real-world deployment or small-scale test. We identify five accountability gaps and propose a minimal reporting standard for more context-specific, participatory, and accountable agentic AI for social good.

翻译：代理型人工智能系统正越来越多地被提出应用于社会公益领域，并常援引联合国可持续发展目标（SDGs）作为全球利益的语汇。然而，公益主张并不能确立系统对其声称服务的社区的责任。我们对2015年至2026年间发表的112篇关于社会公益代理型人工智能的论文进行了系统性调查研究。我们发现一种道德地理学上的不对称：在那些当地政治、法律和文化背景最为关键的领域中，论文反而最不倾向于明确地理背景。在整个文献库中，112篇论文中有82篇（73%）未指明任何地理背景。与健康或物理/生态SDG相关的论文在37%-40%的情况下指明了地理背景，而与制度和社会政策SDG相关的论文仅有13%如此。SDG 16——和平、正义与强大机构，既是文献库中覆盖最多的目标，也是地理背景明确率最低的目标。我们将此解释为道德抽象化：社会公益代理型人工智能往往将制度性公益视为普适性的，其程度不同于对健康或生态公益的处理。第二个发现加剧了这一问题：112篇论文中仅有28篇（25%）报告了任何实际部署或小规模测试。我们识别出五个问责缺口，并提出了一套最低报告标准，以推动更具背景依赖性、参与性及更负责任的社会公益代理型人工智能。