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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallA useful local-government AI policy does more than ban or permit a tool: it assigns accountable owners, requires review of each proposed use, sets rules for tools and data, protects people affected by consequential decisions, and explains how the government will monitor and correct problems. Start with an inventory and approval process, then use risk-based review to decide what employees may do, what needs safeguards, and what should not be automated. The details must be checked against the jurisdiction’s own laws and responsibilities.
1. Set the policy’s scope before choosing rules
Define what counts as AI broadly enough to include more than stand-alone chatbots. Consider predictive systems, generative AI, automated decision support, vendor-operated services, and AI features embedded in software the government already buys. A policy limited to employees typing into a chatbot can miss systems that influence decisions behind the scenes.
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Identify who and what the policy covers: employees, contractors, volunteers, departments, applications, pilots, procurement, development, and ongoing use. Define terms in plain language, and distinguish between AI that drafts or summarizes material and systems that recommend or make decisions about people.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteExamples show different ways to draw the boundary. Boston and Miami-Dade publish employee-oriented generative-AI guidance, while Alameda County describes a broader policy scope covering procurement, development, implementation, and use. A municipality or county should choose language that matches its operations rather than copying another government’s scope unchanged.
2. Assign ownership and decision authority
Name one office to own the policy and keep it current. It might be a technology, data, or administrative office, depending on the government’s structure. Then establish a review group with the expertise needed to assess proposed uses. Depending on local capacity, that group may include IT, cybersecurity, privacy, legal counsel, procurement, records management, human resources, accessibility, service departments, and public representatives.
Make decision rights explicit. Employees need to know who may propose a use, who reviews it, who approves it, who can impose conditions, and who can suspend it. Separate the person requesting a system from the authority that accepts its risks. For a small government without a standing committee, name specific reviewers and an escalation route rather than leaving approval to informal judgment.
UNESCO’s Recommendation on the Ethics of Artificial Intelligence calls for stakeholder participation and impact assessment. Indiana’s state-government process illustrates clear policy ownership and readiness review; it is an implementation example, not a requirement for local governments.
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3. Require intake and maintain an AI inventory
Require departments to submit a use case before buying, trialing, connecting, or deploying an AI system. An inventory makes it possible to see where AI is used, who is accountable, and whether the system’s purpose or risk has changed.
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For each proposal, capture:
- The task, intended benefit, requesting department, and accountable owner.
- The system, vendor, model or service, external integrations, and whether the government can change or disable it.
- The data involved, including personal, confidential, privileged, law-enforcement, procurement, or other nonpublic information.
- Who may be affected, including residents, employees, applicants, and people with disabilities.
- The system’s role in the workflow: drafting, searching, recommending, prioritizing, or making a decision.
- Planned human review, public disclosure, records handling, monitoring, and an exit or rollback plan.
Track approval status, conditions, review dates, incidents, and material changes in the inventory. 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 useful intake distinction to their own approval structure.
4. Review the use and its consequences, not just the product
The same AI service can present different risks depending on its task, data, users, and effect on people. Before approval, ask whether AI is appropriate at all, what happens if it is wrong, and whether a simpler or less intrusive process would meet the need.
NIST’s voluntary AI Risk Management Framework organizes work into four functions: Govern, Map, Measure, and Manage. A local policy can use these as a practical sequence: establish responsibility and rules; understand the context and affected people; evaluate performance and risks; then apply safeguards and monitor outcomes. NIST has reported that AI RMF 1.0 is being revised, so check its current status when adopting it. It is a framework, not a law or an unchanging standard.
Scale review to potential harm. A low-impact internal drafting aid may need a shorter review than a system that ranks applicants or influences public services. For any proposed use, document the assessment, mitigation measures, remaining risks, and the official who accepts them.
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| Review area | Questions to answer |
|---|---|
| Purpose and fit | What problem is the system meant to solve? Is AI necessary, and is the proposed use within the vendor’s documented capabilities? |
| People and rights | Who could be affected? Could the use create unequal outcomes, impede access, or affect rights, benefits, employment, safety, or other important interests? |
| Data and privacy | What information enters the system, where does it go, who can access it, and how is it retained or reused? |
| Reliability and security | How will staff test accuracy and limitations for this task? What security, failure, or vendor-change scenarios need a response? |
| Accessibility and service | Can people with disabilities use the service? Does it work for the languages and circumstances of the residents it serves? |
| Accountability and remedy | Who checks the result, who makes the final decision, and how can an affected person seek correction or human reconsideration? |
UNESCO recommends impact assessment, due diligence, public participation, continuing monitoring, and remedies. These principles help shape a local review, but the jurisdiction should determine its legal duties with its own counsel.
5. Give employees rules they can follow
Publish a current list of approved tools and permitted work uses, with a clear route to request an exception or a new review. A tool’s public availability does not mean it is approved for government work. Specify which information classes may not be entered into unapproved services, including sensitive personal data, confidential or privileged material, law-enforcement information, and nonpublic procurement content.
Set practical expectations for use:
- Employees remain responsible for work produced with AI and must verify facts, calculations, citations, and recommendations before official use.
- Staff must protect source material and follow applicable privacy, security, records, and procurement rules.
- Employees must disclose AI assistance when required by policy or law and must not present generated content as independently verified.
- Prompts, outputs, evaluations, and related materials must be handled under applicable government records and retention rules when they qualify as records.
- Employees should stop and seek review when a use involves sensitive data, a consequential decision, an unapproved tool, or an unexpected system behavior.
Miami-Dade County’s employee guidance illustrates operational rules that direct staff to approved tools, encourage collaboration with IT, require training, and call for fact-checking before official use.
6. Set boundaries for decisions affecting people
State plainly that AI assistance does not transfer accountability away from the public employee or government. Boston’s policy says, “The use of GenAI does not absolve an employee of accountability for the accuracy, ethics, or outcomes of their assignments.” Its policy also prohibits generative AI from determining eligibility for constituent services or benefits. That is an example of a clear local boundary, not a rule that automatically applies everywhere.
Identify prohibited uses and uses requiring enhanced approval in terms employees recognize. Consider eligibility for services or benefits, employment decisions, law enforcement, surveillance, decisions affecting rights, and public-facing advice. For any permitted high-impact support, define who must review the underlying information, what the reviewer must verify, who makes the final decision, and how a person can ask for human reconsideration. Do not treat a nominal human sign-off as meaningful oversight if staff cannot understand or challenge the system’s recommendation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Plan for transparency, records, and redress
Decide when residents should be told that AI is involved, especially when it supports a public-facing service or influences a decision about them. Where feasible, publish an inventory of approved systems and explain each system’s purpose, limits, responsible office, and route for questions or complaints. Give affected people a way to seek correction or review by a person.
Coordinate with records officers before deployment. Determine how prompts, outputs, evaluations, vendor documentation, and decision records will be retained, searched, disclosed, and protected under local public-records and retention rules. A vendor’s default deletion or retention settings do not by themselves settle the government’s obligations.
Texas DIR provides an example of state-specific notice obligations for covered deployments. Those requirements should not be generalized to other jurisdictions; local counsel should confirm which notice, ethics, records, and disclosure rules apply.
8. Train staff, monitor systems, and revise the policy
Train employees before granting access, covering approved tools, data restrictions, output verification, records, disclosure, and how to report a concern. Refresh training as tools, policy, or risks change. Give employees a straightforward channel to ask questions, escalate uncertain uses, and report incidents without having to guess which office is responsible.
For deployed systems, monitor performance, complaints, security events, disparate effects, and changes in purpose, data, vendor, or system behavior. Set review dates and define conditions that trigger an earlier reassessment. Keep the ability to pause, restrict, or roll back a use if a safeguard fails or the risk becomes unacceptable. Miami-Dade’s guidance includes employee training and feedback; UNESCO recommends continuing monitoring and evaluation.
9. Tailor the policy to local law and capacity
There is no single policy that fits every city or county. Requirements depend on jurisdiction and government function. Before adoption, have local counsel and relevant officials check public-records and retention law, privacy and data-protection obligations, procurement rules, civil-rights and accessibility duties, labor requirements, and sector-specific restrictions.
Governments can draw on public-sector examples without treating them as ready-made law. The GovAI Coalition resources offered through San José include policy, governance, impact-assessment, incident-response, and elected-official materials aligned with the NIST AI RMF; they are starting points that need local tailoring. Indiana’s readiness and records guidance can likewise inform process design without imposing state policy on local governments. UNESCO’s Recommendation states that “Member States should support local governments in the development of local policies, regulations and laws in line with national and international legal frameworks.”
A workable first version need not prescribe every future AI use. It should establish a clear owner, mandatory intake, proportionate review, employee rules, boundaries for consequential uses, transparency and redress, incident handling, and a scheduled path to update the inventory and policy.
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