A useful local-government AI policy does more than ban or endorse a tool: it assigns responsibility, requires review of each proposed use, sets clear employee rules, protects people affected by AI-supported decisions, and explains how systems will be monitored. Start with a government-wide inventory and intake process, then use risk-based review to decide what may be used, under what controls, and when a use must be rejected. The NIST AI Risk Management Framework (AI RMF) is a voluntary way to organize that work—not a law or a substitute for local legal review.
What should an AI use policy cover?
Write the policy to cover the work the government does, not only the newest chatbot. State whether it applies to generative AI, predictive or analytical systems, automated decision support, AI features built into purchased software, and systems operated by vendors. Include the people and organizations covered—such as employees, contractors, volunteers, departments, and agencies—according to the jurisdiction’s authority.
Define terms in plain language. A definition limited to software explicitly marketed as “AI” can miss automated features embedded in ordinary products. The policy should address the full lifecycle: procurement, development, testing, implementation, use, monitoring, and retirement. Alameda County’s policy page describes coverage across procurement, development, implementation, and use; Boston and Miami-Dade offer employee-facing generative-AI guidance with narrower emphases.
Set a scope broad enough to catch a proposed use before a department buys, trials, configures, or deploys it. Existing systems should also enter the inventory when their use changes or an AI feature becomes available.
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Who owns the policy and approves AI uses?
Name one office as the policy owner and establish a cross-functional review group. Depending on local structure, that group may include information technology, cybersecurity, privacy, legal counsel, procurement, records management, human resources, accessibility, service departments, and public representatives. Participation should match the risks and responsibilities involved rather than becoming a standing meeting with no decision authority.
Document the decision rights: who may propose a use, who performs the review, who approves it, who can impose conditions, and who can pause or stop it. Identify a route for departments to ask questions and seek exceptions. The policy owner should be responsible for keeping the policy and inventory current, while system owners remain responsible for approved uses in their departments.
UNESCO’s 2021 Recommendation on the Ethics of Artificial Intelligence calls for stakeholder participation, impact assessment, continuing oversight, and local policy development aligned with applicable legal frameworks. Indiana’s state-government process illustrates clear ownership: its Office of the Chief Data Officer monitors policy, with privacy and performance support. That arrangement is an example, not a required organizational chart for cities or counties.
How should a government review a proposed AI use?
Require an intake before purchase, trial, or deployment. Review the specific task and decision context, not just the vendor or product name: the same system can present different risks when used for drafting an internal summary versus influencing a resident’s access to a service.
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Collect enough information to make a decision
Use a standard intake form that asks for:
- The department, accountable owner, task, intended benefit, and alternatives to AI.
- The system, vendor, model or product version where known, external integrations, and whether the tool is already approved elsewhere in government.
- The people affected, including residents, employees, applicants, and groups who may face different impacts.
- Data the system receives or produces, including personal, confidential, privileged, law-enforcement, procurement, or other nonpublic information.
- The system’s role in the work: whether it drafts, summarizes, ranks, recommends, predicts, or makes or triggers a decision.
- Planned human review, disclosure to affected people, recordkeeping, monitoring, and a way to end the use or move to an alternative.
Keep a government-wide inventory of proposed and active uses. Record each use’s owner, purpose, approval status, conditions, review date, and incident history. Indiana’s state guidance distinguishes requests for systems not yet approved from requests to use systems already approved elsewhere in state government; local governments can adapt that intake distinction to their own processes.
Match review depth to potential harm
Ask whether AI is appropriate for the task at all. For a use that proceeds to assessment, examine privacy and security, data quality, accuracy and reliability, bias and disparate effects, accessibility, explainability, labor and service impacts, and possible effects on rights. Document mitigations, the person responsible for each, and any remaining risk. A high-impact use may need testing, legal review, consultation, or public participation before a decision; a low-impact internal drafting use may need a lighter review and strong output-checking rules.
The NIST AI RMF organizes risk work into four functions: Govern (assign accountability and policies), Map (understand context and impacts), Measure (assess and test risks), and Manage (prioritize and respond). NIST describes the framework as voluntary and reports that AI RMF 1.0 is being revised, so check NIST’s current framework status when adopting it. UNESCO likewise recommends impact assessment, due diligence, monitoring, public participation, and remedies. Neither framework replaces the jurisdiction’s own legal analysis.
What rules should employees follow?
Publish a current list of approved tools and the work they may be used for. Explain how an employee can request review of a new tool or use case. Be specific about information employees must not submit to unapproved services, including sensitive, personal, confidential, privileged, law-enforcement, procurement, and other nonpublic material, as appropriate under local rules.
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For approved uses, require employees to check outputs against reliable source material before relying on them in official work. Staff should protect source information, correct errors, and not treat plausible-sounding text or recommendations as verified facts. Explain when AI assistance must be disclosed and how prompts, outputs, evaluations, and related records are handled under applicable records and retention rules.
Miami-Dade County’s employee guidance illustrates practical operating rules: use county-approved tools for work-related purposes, collaborate with IT and other departments, complete training, and fact-check outputs before official use. Those are useful policy elements to adapt, not a substitute for a local approved-tool list or records rules.
Where should a policy draw human-review boundaries?
State plainly that using AI does not transfer accountability from the public employee or government. Define when a qualified employee must check underlying information, assess relevant context, and make the final decision rather than merely accept a system’s recommendation.
Identify prohibited or tightly controlled uses in terms employees recognize. Consider uses that could affect access to public services or benefits, employment, rights, law enforcement, surveillance, or safety. For each restricted use, specify the approval threshold, safeguards, or prohibition; do not rely on a broad promise to “use AI responsibly.” Give affected people a way to ask for human reconsideration where the use can materially affect them.
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Boston’s Generative Artificial Intelligence Policy provides a municipal example: it makes employees responsible for accuracy, ethics, and outcomes, and prohibits generative AI from determining constituent eligibility for services or benefits. That is Boston policy, not a universal legal rule.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should the policy address transparency, records, and redress?
Decide when residents should be told that AI supports a public-facing service or decision. For a consequential use, explain what the system does in understandable terms, what role a human plays, and how an affected person can ask questions, correct information, or seek review. Consider publishing the AI inventory where feasible, with appropriate protection for information that should not be public.
Coordinate with records officers before use. Determine how prompts, outputs, system evaluations, vendor communications, and decision records are retained and disclosed under the jurisdiction’s public-records and retention requirements. Do not assume that a vendor’s deletion setting settles the government’s recordkeeping obligations.
UNESCO recommends transparency, traceability, oversight, and remedies. Texas DIR’s AI resources describe notice obligations for certain covered deployments under Texas-specific requirements. Governments outside Texas should not treat those requirements as universal; local counsel should confirm the rules that apply to the jurisdiction and use.
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How should local leaders use other governments’ examples?
Examples can help turn broad principles into workable employee rules, but their authority and scope differ. Use them as models to adapt, not as ready-made legal policy.
| Example | What it illustrates | How to use it |
|---|---|---|
| City of Boston | Employee accountability and a specific boundary against using generative AI to determine eligibility for constituent services or benefits. | Use the clarity of its rules as a drafting example; decide locally which uses to prohibit or control. |
| Miami-Dade County | Approved tools, collaboration with IT, employee training, and checking outputs before official use. | Translate these operating expectations into a local tool list, training, and verification process. |
| Texas DIR | An acceptable-use policy example and Texas-specific materials on ethics standards and notices in specified cases. | Adapt its policy material cautiously and verify local law; Texas requirements do not automatically apply elsewhere. |
| San José / GovAI Coalition | Adaptable policy, governance, impact-assessment, incident-response, and elected-official checklist resources described as aligned with the NIST AI RMF. | Use templates as starting points for local tailoring, not legal advice or a substitute for review. |
| State of Indiana | Readiness assessment before deployment, NIST alignment, and a process for requests involving already-approved systems. | Consider adapting its intake and ownership concepts to local authority and staffing. |
How should the policy be maintained after adoption?
Train employees before granting access and refresh training as tools, procedures, and risks change. Explain the approved-tool process, data restrictions, output checking, human-review duties, disclosure and records rules, and how to report a concern. Make the route for help or escalation easy to find.
Provide a channel for reporting errors, privacy or security events, unexpected impacts, and complaints. Assign someone to triage reports, preserve relevant records, investigate, and decide whether the use should be corrected, restricted, paused, or stopped. Keep a rollback or alternative workflow available for systems used in important services.
Set a periodic policy and inventory review, and require an earlier reassessment when the system, vendor, data, purpose, law, or available evidence changes. Monitor approved uses for performance, disparate effects, security events, and complaints. A decision to approve a system once should not be treated as permanent approval for every later use.
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What legal checks belong in local review?
The applicable requirements depend on the jurisdiction and the government function. Before adopting the policy or approving a use, have local counsel and responsible offices check public-records and retention rules, privacy and data-protection law, procurement requirements, civil-rights and accessibility duties, labor rules, and sector-specific restrictions. Also confirm any notice or consultation obligations that apply to the particular service or decision.
UNESCO’s Recommendation supports local policy development in line with national and international legal frameworks. NIST’s AI RMF can organize voluntary risk-management work, but neither it nor another government’s policy determines what local law requires.
