Prompts for Public-Sector LLMs Should Be Governed as Commons
For public-sector organizations deploying LLMs, the paper addresses the lack of transparency and accountability in prompt governance, proposing a framework for auditable and contestable prompt management.
The paper argues that prompts for public-sector LLMs should be governed as commons, proposing a versioned repository (Prompt Commons) with provenance metadata and licensing. Using a pilot dataset of 443 human prompts (3,317 after augmentation), it demonstrates three governance states and a negotiation-oriented ensemble method.
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.