What AI Speaks for Your Community: Polling AI Agents for Public Opinion on Data Center Projects

Zhifeng Wu, Yuelin Han, Shaolei Ren

Keywords: LLM-based agents polling, Data center deployment, Community-informed planning

TL;DR: We propose an AI polling framework to assess community opinion on data centers, the facilities behind AI.

Abstract: 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, helping 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.

Submission Number: 121