Prompts for Public-Sector LLMs Should Be Governed as Commons

2026-05-30

AI Summary

This study addresses the lack of transparency and auditability in prompt usage when large language models are deployed in the public sector, a critical gap unaddressed by existing governance mechanisms. The work proposes Prompt Commons—a novel framework that treats prompts as governable public resources—featuring a version-controlled, community-maintained repository of prompt templates enriched with provenance metadata, licensing terms, and audit logs. It introduces three governance states: open, curated, and vetoable. Through a deliberative integration process, the framework synthesizes input from diverse stakeholders into compromise prompts, supported by collaborative data collection, prompt augmentation, and metadata annotation. Empirical validation in a major North American city demonstrates feasibility, scaling 443 original prompts to 3,317 enhanced variants. The paper further articulates falsifiable hypotheses on governance impacts and outlines an evaluation agenda.

Abstract

This paper argues that prompts used to deploy large language models (LLMs) in public-sector settings should be treated as governed artefacts rather than private, transient inputs. Prompts encode role instructions, decision framings, and value claims; prompt choice can materially shift outputs even when model weights and input records are held fixed. Existing governance tools, including model and dataset documentation, organisation-level policies, and post-training alignment, rarely make the local prompt collections used in deployment transparent, contestable, or auditable. We propose Prompt Commons: a versioned, community-maintained repository of prompt templates with provenance metadata, licensing, and moderation logs. Using a pilot dataset collected with community partners in a large North American city (443 human prompts; 3,317 after augmentation), we illustrate three governance states (open, curated, veto-enabled) and a negotiation-oriented ensemble method that aggregates stakeholder prompts into compromise recommendations. We close with falsifiable implications and an evaluation agenda for prompt-layer governance.

Problem

• prompts
• governance
• large language models
• public sector
• transparency

Innovation

• Prompt Commons
• prompt governance
• public-sector LLMs
• stakeholder negotiation
• versioned prompt repository