# Prompt Commons: Collective Prompting as Governance for Urban AI

arXiv: https://arxiv.org/abs/2509.12415 · HTML: https://mlsanity.com/paper/2509.12415/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2025.

## Built on by (most cited first, 2 of 2)

- [Do Vision-Language Models See Urban Scenes as People Do? An Urban Perception Benchmark](https://mlsanity.com/paper/2509.14574/) (2025)
- [Policy-as-Skill: Governed LLM Decision Support with Evidence, Deterministic Control, and Audit](https://mlsanity.com/paper/2609.27087/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) are entering urban governance, yet their outputs are highly sensitive to prompts that carry value judgments. We propose Prompt Commons - a versioned, community-maintained repository of prompts with governance metadata, licensing, and moderation - to steer model behaviour toward pluralism. Using a Montreal dataset (443 human prompts; 3,317 after augmentation), we pilot three governance states (open, curated, veto-enabled). On a contested policy benchmark, a single-author prompt yields 24 percent neutral outcomes; commons-governed prompts raise neutrality to 48-52 percent while retaining decisiveness where appropriate. In a synthetic incident log, a veto-enabled regime reduces time-to-remediation for harmful outputs from 30.5 +/- 8.9 hours (open) to 5.6 +/- 1.5 hours. We outline licensing (CC BY/BY-SA for prompts with optional OpenRAIL-style restrictions for artefacts), auditable moderation, and safeguards against dominance capture. Prompt governance offers a practical lever for cities to align AI with local values and accountability.