Whose Good, Whose Place? The Moral Geography of Agentic AI for Social Good
For researchers and practitioners in AI for social good, this paper highlights a systematic lack of geographic accountability and deployment validation, which undermines claims of serving local communities.
A survey of 112 papers on agentic AI for social good (2015-2026) reveals that 73% specify no geographic context, with institutional SDGs (e.g., SDG 16) having the lowest specification rate (13%), and only 25% report any real-world deployment. The authors identify five accountability gaps and propose a minimal reporting standard.
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.