What AI Speaks for Your Community: Polling AI Agents for Public Opinion on Data Center Projects
On the map: LLM agents and tool use → Social simulation and multi-agent debate
Abstract (arXiv)
The intense computational demands of AI, especially large foundation models, are driving a global boom in data centers. These facilities bring both tangible benefits and potential environmental burdens to local communities. However, the planning processes for data centers often fail to proactively integrate local public opinion in advance, largely because traditional polling is expensive or is conducted too late to influence decisions. To address this gap, we introduce an AI agent polling framework, leveraging large language models to assess community opinion on data centers and guide responsible development of AI. Our experiments reveal water consumption and utility costs as primary concerns, while tax revenue is a key perceived benefit. Furthermore, our cross-model and cross-regional analyses show opinions vary significantly by LLM and regional context. Finally, agent responses show strong topical alignment with real-world survey data. Our framework can serve as a scalable screening tool, enabling developers to integrate community sentiment into early-stage planning for a more informed and socially responsible AI infrastructure deployment.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 12 of its 48 references that are in the map. First posted on arXiv 27 Nov 2025.
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12 of 48 references.
References · 12
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
- 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
- 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 777
- 2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
- 2024ElectionSim: Massive Population Election Simulation Powered by Large Language Model Driven Agentscited by 35
- 2024Large Language Models are Biased Because They Are Large Language Modelscited by 26
- 2024Bias Unveiled: Investigating Social Bias in LLM-Generated Codecited by 25
- 2023Large Language Model (LLM) Bias Index -- LLMBIcited by 14
- 2023Sentiment analysis and opinion mining on educational data: A surveycited by 8
- 2025PolicyPulse: LLM-Synthesis Tool for Policy Researcherscited by 4
- 2025Breaking Down Bias: On The Limits of Generalizable Pruning Strategiescited by 2
- 2025The Cloud Next Door: Investigating the Environmental and Socioeconomic Strain of Datacenters on Local Communitiescited by 2
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