A city AI-use policy should establish who may use AI and for what, require review before systems are bought or deployed, protect city and resident data, preserve meaningful human accountability, and explain how the city will monitor, disclose, and govern use over time. It should be enforceable through approval steps, staff responsibilities, and vendor contracts—not just a list of principles.
There is no single municipal template. Local law and existing rules on privacy, security, procurement, public records, accessibility, employment, and civil rights determine what a city must require. Portland, Boston, and Seattle offer useful examples of different policy approaches.
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Start with scope, purpose, and accountable owners
State why the city uses AI and which uses the policy governs. Define AI broadly enough to include predictive and recommendation systems, generative tools, automated decision systems, and AI features embedded in existing software. Clarify whether coverage includes city employees, contractors, vendors, and partners when they do city work or handle city information, and identify any narrow exclusions.
Portland’s rule covers AI systems that process city data, support city operations, or interact with staff or the public, including systems operated on the city’s behalf. That breadth helps prevent a tool from escaping review merely because it is vendor-hosted or bundled into another product. Portland’s AI-use rule is a reference point, not a universal legal template.
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Name an executive sponsor and an operational policy owner. Assign responsibilities for department requests, technology review, procurement, information security, privacy, legal advice, public records, equity or civil-rights review, and public communications. Give designated officials authority to approve, condition, pause, or stop a use when safeguards are inadequate.
Require review before a pilot, purchase, or deployment
Make departments document the system, intended purpose, expected benefit, affected people, data inputs, vendor, decision authority, and proposed safeguards before starting a pilot or acquisition. The review should evaluate the particular use case, not assume a tool is safe or unsafe in every context.
Scale scrutiny to potential impact. A writing assistant used to draft an internal meeting agenda does not present the same stakes as a system that influences eligibility for services, employment, health, safety, or finances. A city can create its own risk tiers and approval gates; the municipal examples do not establish one universally required taxonomy.
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Coordinate AI review with existing security, privacy, financial, legal, equity, and surveillance reviews. Portland expressly says its initial AI risk assessment does not replace or take precedence over other required risk assessments. The policy should identify who can approve, deny, add conditions, and require reassessment.
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Specify what information may be entered into which tools. Apply the city’s existing requirements for personal, confidential, privileged, law-enforcement, health, employment, and other sensitive information. For each system, document what data it receives, who can access it, how long it is retained, whether it is reused, and how it can be deleted.
Do not rely on broad vendor assurances where contract terms can set the rules. Portland requires written city authorization for vendor use of city information to train models and describes disclosure, contract controls, and risk-proportionate audit or verification rights. Boston’s generative-AI policy differentiates approved tools by data sensitivity and bars external tools for city work. Boston’s employee guidance illustrates a tool-and-data control model.
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Require AI-specific procurement review even when a feature is free, bundled, or added to an existing platform. Ask vendors for data-flow and retention details, training-use disclosures, system limitations, security controls, testing evidence, update practices, and incident notification commitments. Where appropriate, contracts should address permitted data use, confidentiality, audit rights, documentation, human oversight, public-records support, accessibility, liability, and termination or exit.
Keep people accountable for generated work and consequential outcomes
Require an employee to review and validate AI-generated material before it is used for city business or published. Meaningful review requires a reviewer with enough expertise and supporting information to assess the output—and authority to reject or correct it. A policy should not treat clicking “approve” as a safeguard.
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Make public-facing use transparent and preserve records
Set expectations for telling residents when AI is involved in a public-facing chat, generated public content, or a service that AI influences. Explain the system’s purpose and known limitations in plain language, and provide a contact or appeal route where people may need help or correction.
Maintain an inventory or public summary of approved uses where practicable and lawful. Portland links transparency to approved-use inventories or summaries and compliance with public-records requirements. Seattle’s principles call for public availability of AI-use documentation. Seattle’s AI principles provide an example of a transparency-oriented approach.
Define how prompts, outputs, review records, system documentation, and decision records are retained under the city’s records schedules and applicable exemptions. The policy should support lawful records access without implying that every prompt or output is automatically disclosable.
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Require assessment of data and outputs for bias, disparate effects, and foreseeable harm to groups affected by a service. Where feasible, test with relevant populations and languages, and involve affected communities in policy design and higher-impact deployments. Provide accessible alternatives and language access so that residents are not forced to use an AI-mediated path they cannot effectively navigate.
Portland requires language access for AI-generated content and services consistent with its language policy and Title VI. Seattle identifies equity and bias evaluation as policy principles. These examples support safeguards, but cities should connect them to their own legal obligations and service context.
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Provide approved tools, role-based guidance, and training before staff use them. Boston makes completion of city AI training a condition of access to certain city-developed and approved tools. Seattle describes employee training as well as measures such as bias audits and user satisfaction.
Establish a reporting route for inaccurate or harmful outputs, privacy and security incidents, and unauthorized tools. Monitor reliability, accuracy, bias, user experience, and changes in system behavior after deployment. Require reassessment when the model, vendor, data, or use case materially changes.
State prohibited uses and control exceptions
List uses the city will not permit, such as unlawful or malicious activity, discriminatory use, unauthorized surveillance, circumvention of privacy or security controls, deceptive public communications, or consequential decisions without suitable human review. Define who can grant an exception, what written rationale and controls are required, and make clear that an exception cannot authorize unlawful conduct. Portland’s rule, for example, reserves exceptions for city administrator approval while prohibiting unlawful, unethical, or policy-contrary conduct.
How municipal approaches differ
| Policy question | Portland | Boston | Seattle |
|---|---|---|---|
| Scope | AI systems processing city data, supporting operations, or interacting with staff or the public. | Focuses on generative-AI tools for employees. | Principles include public documentation and workforce training. |
| Control model | Initial risk assessment with safeguards scaled to risk. | Tool approval tied to data sensitivity and an AI inventory. | Approved procurement channels with AI-specific considerations. |
| Transparency | Communication about purpose and use, including inventories or summaries. | Employee guidance and tool controls. | AI-use documentation intended to be publicly available. |
| Procurement and data | Vendor disclosures, technical documentation, and limits on model training with city data. | Distinguishes tool access by data sensitivity. | Requires approved procurement channels. |
| Human accountability | Human review proportionate to risk for consequential outcomes. | Employees remain accountable for work and downstream impact. | Not stated in the cited principles. |
Turn the policy into an operating checklist
- Define coverage: specify systems, users, city work, vendors, embedded features, and exclusions.
- Assign owners: name officials responsible for intake, review, procurement, privacy, security, records, equity, and oversight.
- Set review gates: require a documented use case and risk review before pilots, purchases, or deployment, alongside other required reviews.
- Set data and contract rules: classify allowed inputs, document retention and reuse, and negotiate enforceable vendor limits.
- Preserve human authority: validate generated work and define review, escalation, notice, and remedy for consequential decisions.
- Publish and retain responsibly: set disclosure, inventory, records, and language-access practices consistent with local law.
- Operate and revisit: train staff, receive incident reports, monitor outcomes, and reassess material changes.
- Enforce limits: identify prohibited uses and tightly govern any exception process.
The exact requirements should be checked against the city’s current rules and legal obligations. Portland’s, Boston’s, and Seattle’s published materials are design references; municipal policies and tool inventories can change.
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