Whose Good, Whose Place? The Moral Geography of Agentic AI for Social Good
2026-05-25T07:23:52Z•a07c5e31ebd332737dea955769735c7d7f9a2788e222cedb2bdbe25bfa346040
AI ethicsLLM safetyLoRASolarChainaccountabilityagentic AIbenchmarksblockchain/energy marketsclinical triagecommunity governancedata integrityexport controlsgenerative AIgeographic contexthealth misinformationlegal remediesmental healthmodel personalizationprivacyreporting standards
What happened
This feed summarizes 11 recent arXiv papers (May 25, 2026) covering risks, evaluations, and policy proposals across agentic AI, generative models in health, LLM benchmarks, and governance. Key findings: a survey of 112 papers on agentic AI for social good reveals a "moral-geographic" asymmetry (73% of papers omit geographic context) and only 25% report real-world tests, with a proposed minimal reporting standard to close accountability gaps. Work on generative AI in the health information journey defines a four-stage risk/opportunity framework, while Iy'aw'oBench (200 synthetic vignettes from
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_cy
- Record identifier
- a07c5e31ebd332737dea955769735c7d7f9a2788e222cedb2bdbe25bfa346040
- Enrichment time
- 2026-05-25T07:23:52Z
- AI-assisted enrichment
- Yes
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