AI Governance Policy Examples

Summary

Government teams struggling to translate AI governance principles into actionable policies now have a practical starting point. This collection from Madison AI provides 16 real-world policy examples specifically designed for government organizations, featuring proven frameworks like the City of San Jose's Generative AI Guidelines alongside communication templates for explaining AI initiatives to stakeholders. Rather than starting from scratch, teams can adapt these tested approaches to their specific jurisdictions and requirements.

What makes this different

Unlike academic frameworks or corporate AI policies, these examples are purpose-built for the unique challenges government teams face. They address public accountability requirements, transparency mandates, and the complex stakeholder dynamics inherent in public sector AI deployment. The collection bridges the gap between high-level AI governance principles and the granular policy language needed for municipal and government implementation.

The resource goes beyond policy text by including communication frameworks—recognition that successful AI governance requires not just good policies but effective ways to explain AI initiatives to elected officials, staff, and the public.

Policy categories you'll find

Operational guidelines cover day-to-day AI use scenarios, vendor management, and procurement decisions. These policies help government workers understand when and how AI tools can be used in their daily responsibilities.

Strategic frameworks address longer-term AI planning, including risk assessment processes, governance committee structures, and decision-making hierarchies for AI investments.

Public-facing policies focus on transparency requirements, explaining AI use to constituents, and maintaining public trust. These are particularly valuable given the unique accountability standards government organizations must meet.

Communication templates provide tested language for explaining AI initiatives to non-technical stakeholders, including elected officials and community groups who need to understand AI impacts without getting lost in technical details.

Who this resource is for

Municipal IT directors and CIOs leading digital transformation initiatives will find practical policy language they can adapt for their cities and counties.

Government policy analysts tasked with drafting AI governance frameworks can use these examples as starting points rather than beginning with blank documents.

City managers and department heads who need to explain AI initiatives to elected officials and the public will benefit from the communication frameworks and stakeholder engagement approaches.

State and local government attorneys working on AI-related legal frameworks can reference these examples to understand how other jurisdictions are structuring their governance approaches.

Getting started with these examples

Begin by reviewing policies from similar-sized jurisdictions or government types. A small city's approach may not scale directly to a state agency, and vice versa. Pay attention to how different examples handle risk categorization—some focus on use case types while others emphasize data sensitivity levels.

Focus first on adapting the communication frameworks before diving into detailed policy language. Successfully explaining your AI governance approach to stakeholders is often more critical than having perfect policy text that no one understands or supports.

Consider which examples align with your organization's existing governance structures. Some policies assume dedicated AI committees while others integrate AI governance into existing IT or risk management processes.

Watch out for

These policies reflect 2024 governance thinking and regulatory landscapes. AI governance requirements are evolving rapidly at federal and state levels, so any adapted policies will need regular review and updates.

The examples may not address jurisdiction-specific requirements like state transparency laws or local procurement regulations. They provide excellent starting frameworks but require customization for local legal and regulatory contexts.

Don't assume that policies working well in one government context will automatically translate to another. Municipal governments face different constraints than state agencies or federal departments.