{"slug": "ejai-judgment-preserving-civic-ai-for-data-center-environmental-justice", "title": "EJAI: Judgment-Preserving Civic AI for Data Center Environmental Justice", "summary": "A formative study with 10 users found that dashboards and single-agent AI tend to settle questions before people examine evidence, prompting researchers to build EJAI, a source-grounded civic AI system for data center environmental justice disputes, according to an arXiv paper (2611.13525v1). In a functional benchmark of 21 queries, EJAI's retrieved evidence supported 98.4% of factual claims and the system acknowledged missing evidence in 32 of 33 cases, though its Corporate Executive persona omitted some evidence conflicting with its assigned priorities. In a within-subjects study, 15 participants acting as residents prepared for a simulated town hall, with some checking cited AI claims while treating map metrics as official and others finding the personas simplified or biased; the authors conclude corpus imbalance and two-sided personas remain open justice concerns.", "body_md": "arXiv:2611.13525v1 Announce Type: cross \nAbstract: Communities in the United States and around the world are contesting data centers, the physical infrastructure of AI. These disputes pose environmental justice issues: residents must weigh unevenly distributed burdens using evidence scattered across metrics, corporate claims, policy documents, and public testimony. A formative study with 10 users showed that dashboards and single-agent AI can settle questions before people examine that evidence. Here we present EJAI, a source-grounded system that links an interactive map, retrieved evidence, missing-data statements, and two stakeholder personas that allow people to inspect evidence and form their own policy recommendations. In a small functional benchmark of 21 queries, retrieved evidence supported 98.4% of factual claims and the system acknowledged missing evidence in 32 of 33 cases. Persona alignment carried a grounding cost: the Corporate Executive omitted some evidence that conflicted with its assigned priorities. In a within-subjects study, 15 participants acting as residents prepared for a simulated town hall. Some checked cited AI claims while treating map metrics as official, some made conditional recommendations with safeguards such as water-use disclosure, and some found the personas simplified or biased. Our results suggest that civic AI can support preparation for public reasoning when it makes evidence, gaps, and disagreement open to inspection, and that corpus imbalance and two-sided personas remain open justice concerns.", "url": "https://wpnews.pro/news/ejai-judgment-preserving-civic-ai-for-data-center-environmental-justice", "canonical_source": "https://www.machinebrief.com/news/ejai-judgment-preserving-civic-ai-for-data-center-environmen-izdm", "published_at": "2026-11-11 05:00:00+00:00", "updated_at": "2026-11-11 06:21:43.117994+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-ethics", "ai-agents", "ai-safety"], "entities": ["EJAI", "arXiv"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/ejai-judgment-preserving-civic-ai-for-data-center-environmental-justice", "markdown": "https://wpnews.pro/news/ejai-judgment-preserving-civic-ai-for-data-center-environmental-justice.md", "text": "https://wpnews.pro/news/ejai-judgment-preserving-civic-ai-for-data-center-environmental-justice.txt", "jsonld": "https://wpnews.pro/news/ejai-judgment-preserving-civic-ai-for-data-center-environmental-justice.jsonld"}}