Cities can encourage useful AI projects without treating speed as the only measure of success. Start with a defined public-service need, consider whether AI is the right tool, assess risks before procurement and as systems change, make vendors answerable, disclose meaningful information, and scale human oversight to the consequences for residents. These are practical governance measures, not a universal legal formula: each city must also follow the law that applies in its jurisdiction.
What does a responsible balance between AI innovation and accountability look like?
Innovation and accountability are not opposing goals. A city can test ways to improve services while requiring evidence that a proposed system serves a real public purpose, safeguards residents, and can be monitored and challenged. Accountability is more than publishing a policy or naming an ethics officer: it requires clear responsibility, effective oversight, and routes for people to question consequential decisions.
The UK Government’s Data and AI Ethics Framework describes accountability in those terms and calls for public-sector work to respect privacy, promote fairness, and protect people and communities from harm. The framework is UK guidance, not a law that automatically applies to every city. It also recommends the Algorithmic Transparency Recording Standard for local government; its stated mandatory scope is central departments and arm’s-length bodies providing public or frontline services or routinely interacting with the public.
How should a city decide whether to use AI?
Begin with the service need
Describe the problem in public-service terms: who is affected, what outcome should improve, and how the city will know whether it has improved. Explain why AI is relevant to that goal. A proposal should remain open to non-AI alternatives, including changing a process, improving existing software, or adding staff capacity.
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This discipline helps avoid buying technology simply because it is available. It also gives the city a basis for judging whether the system is worth testing and whether it should continue after deployment.
Assess impacts at decision points
Impact assessment should begin during project design, not after a vendor has been selected. Revisit it during procurement, before deployment, when the system or its use changes materially, and while it operates. The assessment should examine data quality, bias, privacy, equality, security, human and socioeconomic impacts, unintended consequences, and whole-life costs.
An assessment is useful only if it can change the decision. Depending on what it finds, the city should be prepared to proceed with safeguards, revise the proposal, choose a different approach, or stop the project. UK AI procurement guidance similarly recommends early assessment and review at key decision points.
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What should cities require from AI vendors?
Procurement is both an innovation lever and a way to set enforceable expectations. UK guidance recommends defining a public-benefit goal, explaining why AI is relevant, considering alternatives, and addressing transparency and explainability to reduce black-box and vendor lock-in risks. Cities can turn those principles into specific questions and contract terms.
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- Purpose and design: What is the system intended to do, and what uses are outside that purpose?
- Data: What data does it use, who can access that data, how is it retained, and may the vendor use city data for training, testing, or improvement?
- Performance: How was accuracy evaluated, and how does performance differ across demographic groups relevant to the service?
- Risks and safeguards: What known biases, limitations, and failure modes exist, and what mitigations are available?
- Oversight and verification: Can the city audit or otherwise verify contractual promises, receive incident reports, and review material system changes?
- Security and exit: What security and retention obligations apply, and can the city move away from the system without losing access to necessary records or becoming dependent on the vendor?
Answers should be specific enough for the city to evaluate and monitor. Contract language can make relevant duties—such as disclosure, data handling, audit access, incident reporting, and exit arrangements—part of the vendor’s obligations rather than informal assurances.
What should an AI transparency record tell the public?
A public record should help residents understand what a system does, why the city uses it, who is responsible, and how to raise a concern. Depending on the system and applicable law, useful disclosures include its purpose, the responsible city office and vendor, data access, assessment findings, identified risks, mitigations, and how outcomes are monitored.
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Transparency has several dimensions: technical information about a system, information about the process for using it, and information about its outcomes. A complex model, including a large language model, may not provide a fully explainable causal chain for every output. Cities should therefore match explainability demands to the system’s use and potential impact, while still disclosing purpose, process, performance, outcomes, and ways to contest consequential decisions.
A register improves visibility; it is not a substitute for impact assessment, audit, or oversight. Nor does publication alone ensure that a resident can understand or challenge a decision. The city should make the information useful to affected people, not only to technical specialists.
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How much human oversight is appropriate?
Oversight should be proportionate to the potential harm. A system that can significantly affect a person’s rights, health, safety, or finances warrants stronger review than a low-impact administrative aid. For consequential uses, cities should provide meaningful human review, a clear route to challenge an outcome, and the authority to pause, change, or stop a system when safeguards fail.
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Human involvement is not meaningful merely because a staff member is nominally in the loop. The reviewer needs enough information, time, and authority to question the system’s output rather than simply accept it. The city should also identify who handles complaints and what happens when review reveals an error or recurring pattern of harm.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do city governance approaches compare?
Cities can combine mechanisms because each serves a different purpose. The examples below illustrate approaches described in official city materials and an OECD comparative note; they are not a single, universally required model.
| Mechanism | What it contributes | City example and qualification |
|---|---|---|
| Public AI register | Visibility into systems, purposes, and responsible entities | Amsterdam and Helsinki maintain public AI registers, according to an OECD comparative note. |
| Algorithmic impact assessment | A structured way to identify and address risks before a system is used | Barcelona requires an algorithmic impact assessment for digital solutions procured by the city, according to the OECD note. |
| Independent ethics advice | An additional review perspective on a system’s ethical implications | Turin’s tourism and hospitality assistant has an independent ethics board, according to the OECD note. |
| Procurement and operational rules | Requirements for assessment, vendor disclosures, oversight, and communication | Portland’s Administrative Rule BTS-4.04 took effect March 6, 2026; its requirements apply to Portland. |
| Public reporting and governance actions | Visibility into planned work and progress reporting | New York City’s 2023 AI Action Plan announced an annual public progress report among its actions; the announcement does not establish completion. |
When choosing among these tools, a city should ask whether the package provides visibility, risk control, contestability, vendor accountability, proportionate safeguards, and enough staff expertise, community input, and independent review to implement it. These are practical comparison criteria, not an official scoring system.
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What do New York City and Portland require?
New York City: an assessed-systems list
A New York City law effective June 23, 2026 requires the Office of Algorithmic Accountability to publish, by March 31, 2027, a list of AI systems it assessed before deployment. Required information includes each system’s description and purpose, developers and managers including vendors, the responsible city entity, assessment date, identified risks and recommendations, and data access—including access by people outside the city. The code calls for annual updates. Inclusion on the list is not a prerequisite for an agency to use a system.
Portland: procurement assessment and vendor disclosure
Portland’s Administrative Rule BTS-4.04 took effect March 6, 2026. It establishes oversight roles, risk and benefit management, communication or inventories of approved AI uses, and an initial risk assessment before AI procurement. Vendors must disclose information about data handling, model behavior, adaptive components, and whether city data is used for training or improvement. The rule prohibits using city data for those purposes unless the city explicitly authorizes it in writing under applicable requirements.
Portland’s rule also requires human review proportionate to risk for consequential decisions and communication of AI use in public-facing systems. It prohibits specified uses, including unauthorized discriminatory use and social scoring. These are Portland-specific administrative requirements, not rules that automatically govern other cities.
What can cities learn from New York City’s 2023 plan?
New York City’s Mayor’s Office announced an AI Action Plan in 2023 with 37 actions. The announcement said 29 of the 37 were intended to start or finish within the following year. Proposed measures included governance addressing bias and disparate impact, an external advisory network, staff skills, AI procurement guidance, and an annual public progress report. Those figures describe the announced plan and its intended timeline, not independently verified completion.
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