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

arXiv: https://arxiv.org/abs/2511.22037 · HTML: https://mlsanity.com/paper/2511.22037/

## Key facts

- No citing papers in the map yet (data through 14 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.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 12 of 12)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://mlsanity.com/paper/2107.07511/) (2021)
- [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://mlsanity.com/paper/2111.00364/) (2021)
- [ElectionSim: Massive Population Election Simulation Powered by Large Language Model Driven Agents](https://mlsanity.com/paper/2410.20746/) (2024)
- [Large Language Models are Biased Because They Are Large Language Models](https://mlsanity.com/paper/2406.13138/) (2024)
- [Bias Unveiled: Investigating Social Bias in LLM-Generated Code](https://mlsanity.com/paper/2411.10351/) (2024)
- [Large Language Model (LLM) Bias Index -- LLMBI](https://mlsanity.com/paper/2312.14769/) (2023)
- [Sentiment analysis and opinion mining on educational data: A survey](https://mlsanity.com/paper/2302.04359/) (2023)
- [PolicyPulse: LLM-Synthesis Tool for Policy Researchers](https://mlsanity.com/paper/2505.23994/) (2025)
- [Breaking Down Bias: On The Limits of Generalizable Pruning Strategies](https://mlsanity.com/paper/2502.07771/) (2025)
- [The Cloud Next Door: Investigating the Environmental and Socioeconomic Strain of Datacenters on Local Communities](https://mlsanity.com/paper/2506.03367/) (2025)

## 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.