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A responsible city AI policy should make clear which tools and people it covers, who approves proposed uses, what safeguards apply at each risk level, and how residents can get information or challenge harmful errors. Treat it as an operating framework—not a statement of values alone—with assigned owners, required review steps, enforceable controls, and ongoing oversight. Adapt it to your city’s laws, administrative structure, procurement rules, records and privacy requirements, labor agreements, and oversight powers.
What should a city AI policy include?
At minimum, write rules for the full lifecycle of AI used by or on behalf of the city: proposing a use, reviewing it, acquiring or building a system, testing it, deploying it, monitoring it, responding to problems, and retiring it. The policy should cover ordinary software with AI features as well as tools marketed explicitly as AI.
Use a structure that lets staff answer practical questions: Is this use covered? Who reviews it? What evidence and safeguards are required? Who is accountable after launch? What can an affected resident do? The UK Government Digital Service’s Data and AI Ethics Framework addresses responsible development, procurement, and use of data and AI in the public sector. Maryland’s state AI policy and Washington, D.C.’s mayoral order offer additional governance examples, but none should be treated as a substitute for local legal review.
1. Define the purpose, scope, and key terms
Begin with the public-service goals the policy supports, such as improving service delivery while protecting residents’ rights and safety. Then state the policy’s reach in plain language. A narrow definition limited to standalone AI products can miss tools bought for another purpose that include automated features, vendor-operated services, or small pilots that later influence official decisions.
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Cover people, systems, and lifecycle stages
Apply the policy to city departments and, where legally and contractually possible, contractors and other parties acting for the city. Cover systems that the city purchases, configures, develops, pilots, operates, or maintains, including systems under consideration before a purchase or launch. Include material changes to an existing system, not just new acquisitions.
Define AI broadly enough to capture relevant technologies without making staff guess. Say explicitly whether the policy covers generative AI, automated decision tools, data-driven systems, and AI features embedded in products that are not primarily sold as AI. Maryland’s policy applies to systems deployed or under consideration and to people involved in purchasing, developing, operating, or maintaining them. The UK framework also encompasses AI, data-driven technology, and automated decision-making. Those examples support broad coverage, but your definitions should fit local law and operational needs.
Set boundaries and exclusions deliberately
If some uses are excluded—such as a tool used only for routine drafting—describe the boundary and any conditions. A tool that summarizes public material for an employee may have a different risk profile from one that drafts a notice affecting benefits or influences a service decision. Even a permitted low-impact use may still need privacy, security, records-retention, and staff-training rules. Specify that a change in purpose, data, users, or decision role triggers renewed review.
2. Turn values into enforceable duties
Keep the principles concise, then pair each one with a required action and a record that can be checked. For example, a fairness principle is more useful when it requires the department to consider unequal effects on affected groups and document what it did in response.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware match| Principle | Policy duty | Evidence to retain |
|---|---|---|
| Public benefit and human-centered service | State the service problem, intended benefit, affected people, and why an AI approach is appropriate compared with alternatives. | Use-case submission and approval rationale. |
| Privacy and data stewardship | Identify data sources, sensitivity, permitted uses, access controls, retention, and any restrictions on using resident or city data. | Data review and privacy assessment, where required. |
| Fairness and equity | Assess who may benefit or be burdened, what evidence is available about unequal impacts, and how identified risks will be addressed. | Impact assessment, test results, and mitigation decisions. |
| Safety and security | Require security review, appropriate testing, safeguards against foreseeable harm, and a response path for vulnerabilities or failures. | Security review, test records, and incident plan. |
| Transparency and accessibility | Explain material city uses in language residents can understand and provide information in accessible formats as required by law and service obligations. | Public notice and accessibility review. |
| Accountability | Name the official responsible for each use and define who can approve, pause, or end it. | Named owner, approvals, and monitoring records. |
These are policy-design choices, not a universal legal checklist. The UK framework emphasizes privacy, fairness, and protection from harm; Maryland lists human-centered design, security and safety, privacy, transparency, equity, and accountability. Translate the principles into duties that your city’s legal and administrative framework can support.
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3. Assign ownership and review responsibilities
A policy without named owners tends to leave responsibility divided among departments, IT, procurement, and vendors. Establish a central governance function and make each operating department accountable for its own proposed uses and deployed systems.
Central governance
Name an executive sponsor with authority to resolve cross-department issues, and a central AI governance owner or body to maintain policy, intake procedures, standards, and the citywide inventory. Give that function a clear route to escalate unacceptable risks and recommend pausing or ending a use. Washington, D.C.’s order establishes a central AI taskforce and agency-specific strategic planning; Maryland calls for agency AI leads working with portfolio, data, and privacy officers.
Department and specialist roles
Require the department sponsoring a use to explain its purpose, identify affected services and people, maintain records, and monitor results. Assign review roles according to the use, drawing on service experts, procurement, legal, privacy, information security, data, accessibility, labor relations, and community engagement. Not every proposal needs every specialist, but the intake process should identify which reviews apply and who signs off.
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State who has final approval, who may impose conditions, and who can suspend use. Separate technical validation from the service owner’s decision to rely on a system. A vendor’s assurances should not replace the city’s own approval or accountability.
4. Require inventory and intake before a pilot or deployment
Make registration and review mandatory before testing, procurement, or production use—not only after a tool has become embedded in a workflow. Require departments to report pilots and material changes as well as fully deployed systems. A central inventory makes it possible to see where systems are used, who owns them, and which need further assessment.
Ask for enough information to decide whether to proceed
A standard intake form should capture:
- The service problem and proposed purpose, including why an AI system is being considered and what non-AI alternatives were examined.
- The department, accountable official, vendor or development team, system status, users, and intended deployment date.
- People and communities affected, the service or decision involved, and whether the tool recommends, ranks, generates, or makes an output used by staff.
- Data inputs and outputs, data sensitivity and sources, and whether resident information or confidential city data will be provided to a vendor or model.
- Expected benefits, foreseeable harms, uncertainty, dependencies, and how the department will tell whether the use is working as intended.
- Applicable procurement, legal, privacy, security, accessibility, labor, records, and public-notice reviews.
Use risk tiers to scale review
Define risk in terms of potential effects, not a vendor’s product label. Consider impacts on rights, safety, essential services, finances, privacy, and critical government operations; also consider the scale of use, the people affected, and how much human judgment remains. Your city must define its own thresholds and required evidence. The tiers below are a practical design pattern, not a common legal standard:
| Illustrative tier | Typical policy treatment |
|---|---|
| Lower impact | Register the use, name an owner, follow baseline privacy and security rules, train users, and reassess if the purpose or data changes. |
| Elevated impact | Require specialist review, documented testing and limitations, defined human review, resident-facing information where appropriate, and a monitoring plan before approval. |
| High impact | Require a comprehensive risk assessment, stronger safeguards, explicit approval by designated officials, ongoing monitoring or audit, clear human authority, and a way to address harmful errors. |
| Unacceptable or unresolved risk | Prohibit the use or pause it until the city can demonstrate that the risk is mitigated and the required safeguards are in place. |
Make clear that a high-impact designation does not itself authorize deployment. Maryland prohibits systems with unmitigable unacceptable risk and conditions high-risk use on robust safeguards, comprehensive risk assessment, and ongoing monitoring. A city can adapt that approach while setting its own definitions, approval authority, and prohibited-use rules.
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Require AI-specific review through approved procurement channels, including when an AI feature is bundled into a product purchased for another purpose. Start with the service problem and requirements rather than a vendor’s proposed solution. A procurement process should establish whether the city has the necessary data, expertise, and ability to validate and monitor the proposed system.
Evaluate the system and the vendor
Ask vendors for documentation relevant to the proposed use: system capabilities and limitations, data practices, security controls, performance evidence, known failure modes, and how they notify customers about material changes. Request evidence that is specific enough for the city to assess the intended deployment; general claims about accuracy or safety may not establish performance for a local population, workflow, or decision.
Use a multidisciplinary review involving the service department, procurement, technical staff, and relevant legal, privacy, security, and accessibility specialists. The UK government’s guidance recommends strategic AI procurement, multidisciplinary teams, and data governance from the outset. Washington, D.C.’s order calls for a mandatory AI procurement handbook addressing tool capabilities, procurement scoping, and performance monitoring.
Put operating obligations in the contract
Set contract terms appropriate to the system and applicable local law. Address data access and permitted use, confidentiality and retention, security, incident notification, documentation, performance reporting, audit or inspection rights, material product changes, service continuity, and exit or data-return arrangements. Clarify which party is responsible for investigating errors and supporting remediation. Include labor and bargaining requirements where applicable. A contract cannot transfer away the city’s duty to govern a public service.
Plan for the end of the relationship at purchase time: understand how the city will retrieve needed records, transition the service, and avoid relying on outputs or infrastructure it cannot maintain. The City of Seattle says its updated AI policy requires employees to acquire technology through approved procurement channels that include AI-specific considerations aligned with city standards.
6. Provide transparency, meaningful human oversight, and resident recourse
Publish accessible information about material city AI uses. For each system, explain its purpose, responsible department, general role in the service or decision, relevant data sources when disclosable, and principal safeguards. Coordinate public explanations with records, privacy, security, and procurement obligations; transparency should inform residents without unlawfully disclosing protected information.
Define the human role precisely
For a use that can materially affect a person, identify who reviews the output, what information that reviewer considers, and what authority they have to reject or correct it. Do not describe a process as human oversight if staff are expected to accept an output without time, information, training, or authority to exercise judgment. State when an AI output cannot be the final decision on its own, consistent with local law and the service involved.
Give residents a usable path to raise concerns
Provide a contact and process for reporting errors, asking how a system affected a service, and contesting a harmful decision. Explain how the city will route a complaint to a person with authority to investigate, correct records or outputs where appropriate, and provide a response. The process should work for people who may not know which technology was involved. The UK framework recommends public information about purpose, data sources, and decision logic, feedback mechanisms, and human oversight in risky or high-impact situations; Washington, D.C.’s order includes public listening sessions for its advisory group.
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7. Train users, monitor systems, and plan for incidents and retirement
Train before access and refresh guidance
Require staff training before they use covered tools. Tailor it to the role: users need to know permitted uses, data restrictions, verification duties, and how to report problems; approvers and system owners need to understand review and monitoring responsibilities. Update guidance when a system, its intended use, or its risks change.
Monitor performance after launch
Approval is not a one-time guarantee. Require an owner to track whether the system continues to meet its purpose and safeguards, including performance, security, complaints, and effects on affected groups where the city has an appropriate basis and method to assess them. Set monitoring frequency and reporting thresholds based on risk. Require reassessment after material changes, serious complaints, incidents, or evidence that expected benefits or safeguards no longer hold.
Respond to incidents and retire responsibly
Define what counts as an AI-related incident, who must report it, who investigates, and who can restrict or suspend use while the city assesses harm. Include escalation to privacy, security, legal, and service leaders as appropriate. Document corrective actions and decide whether affected residents or oversight bodies must be notified under local requirements.
Set retirement criteria for systems that no longer meet policy requirements, no longer serve the approved purpose, or cannot be monitored adequately. Assign responsibility for stopping use, preserving required records, managing vendor exit, and maintaining the public service through another process. Maryland requires ongoing monitoring or auditing of high-risk AI and includes sunset procedures for systems that no longer meet requirements; Washington, D.C.’s order calls for staff training, cybersecurity review, and recurring agency plans.
How do real government policy examples differ?
These examples illustrate different governance choices, not interchangeable legal authorities. Use them to compare design patterns, then localize the details to your city’s laws and institutions.
| Example | Documented approach | What a city can examine |
|---|---|---|
| Maryland state AI policy | Broad coverage of systems under consideration and deployed systems; agency AI leads; explicit unacceptable- and high-risk concepts; safeguards and ongoing oversight for high-risk uses. | Whether to require early registration, agency-level owners, explicit risk thresholds, and defined conditions for prohibited or high-risk uses. |
| Washington, D.C. mayoral order | Central AI taskforce, agency-specific planning, procurement-handbook milestone, staff training, cybersecurity review, and public listening sessions for its advisory group. | How central coordination can work alongside department plans, procurement standards, public engagement, and recurring review. |
| City of Seattle AI policy | Updated general AI policy incorporates the city’s earlier generative AI policy and requires approved procurement channels with AI-specific considerations. | How to integrate generative AI rules into a broader policy and embed AI review in ordinary purchasing controls. |
| UK Government Digital Service framework | Cross-government guidance for responsible development, procurement, and use of data and AI, including procurement, public information, feedback, and human oversight considerations. | How to organize multidisciplinary review and connect procurement, data governance, and public accountability. |
Because the UK framework is national guidance and Maryland is a state policy, neither automatically governs a city elsewhere. Even city examples reflect their own authorities and administrative arrangements.
How to put the policy into operation
- Map authority and obligations. Identify the city officials and bodies with relevant powers, then have counsel and specialists map applicable privacy, records, procurement, accessibility, labor, and service-specific requirements.
- Choose accountable owners. Name the executive sponsor, central governance owner, and department-level system owners; give them written decision and escalation authority.
- Adopt scope, intake, and inventory rules. Define covered uses and require registration before pilots or deployment, including material changes and embedded features.
- Set risk criteria and approval gates. Specify what evidence and safeguards each tier requires, which uses are prohibited or paused, and which officials can approve exceptions or stop use.
- Update procurement and contracts. Add AI review to purchasing workflows and include system-specific documentation, data, security, monitoring, incident, and exit terms.
- Publish resident information and recourse routes. Decide what information will be public, how residents can ask questions or challenge harmful errors, and who must respond.
- Fund training and continuing oversight. Assign monitoring, incident response, reassessment, audit, and retirement responsibilities before approving a system for service use.
Review the policy on a defined schedule and when law, technology, or city practice changes. A policy is operational only when departments have a workable intake route, decision-makers have authority, and owners can show what happened after approval.
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