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Start with the public purpose, not the vendor’s features
Describe the service problem the city needs to solve, who would use the system, which residents might be affected, and what public benefit the city expects. Then ask whether a non-AI approach or a less data-intensive design could meet the same need. If the city cannot explain the intended benefit and why AI is being considered, it is not ready to assess whether the proposed data use is proportionate. The UK Government’s AI procurement guidance offers a process model that includes documenting user needs and public benefit; it is useful guidance, not a rule governing every city.
Map the data and the system boundary
Make an inventory of information the system will receive, generate, or expose. Include its source, sensitivity, access permissions or consent basis, retention and reuse, and who can access it. Follow the information beyond the city’s own systems: identify external datasets, model providers, hosting services, subcontractors, and any other dependencies. Ask the supplier how data is used to operate, improve, or train the service, and what happens to it when the contract ends.
Two procurement questions are especially useful: “Do we have consent to use the data sources required by the solution?” and “Do we fully understand the implications of using external data, models or solutions?” They appear in the AI Procurement in a Box toolkit. Treat them as prompts for a documented review, not as a complete legal test: the applicable permissions and legal requirements depend on the city and use case.
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Look for ways to reduce exposure
Ask whether the same public purpose can be achieved with less sensitive information, less collection, or fewer people’s records. Where appropriate, consider aggregation or masking. Record what data is necessary and why, rather than accepting a supplier’s requested inputs as a given.
Identify affected people and the consequences of error
Consider more than residents whose information is processed. Include people subject to recommendations or decisions, employees who may rely on outputs, and groups who could bear unequal consequences. Examine data quality, possible errors and bias, human review, whether people can challenge an output, and foreseeable unintended uses.
Privacy is one part of a broader trustworthiness assessment. NIST’s AI RMF FAQs identify characteristics including security, fairness, accountability, transparency, explainability, safety, validity, and reliability, and describe their relevance across pre-design, design and development, deployment, use, and test and evaluation. For a city, that means asking not only whether data is confidential, but also whether an output could be wrong, difficult to explain, hard to contest, or harmful in practice.
Compare proposals on the same questions
If the city is considering more than one proposal, assess them against a common set of criteria instead of comparing vendor claims in isolation:
- How much data does each proposal require, and how sensitive is it?
- What external data, models, hosting, or subcontractors does it depend on?
- Who could be affected, and what are the consequences of a mistaken output?
- What evidence is available about accuracy and bias testing?
- How transparent is the system, what human review is possible, and can an affected person contest an output?
- Can a less intrusive alternative achieve the same objective?
- What ongoing support, monitoring, and whole-life resources will the city need?
These criteria synthesize the UK procurement guidance, the NIST FAQs, and the AI Procurement in a Box toolkit; they do not rank specific suppliers.
Scale the review to the proposal’s risk
Screen every proposal, then deepen the assessment when the data or potential impact warrants it. Factors to consider include data sensitivity; the number and vulnerability of affected people; effects on access to services or rights; how much the process is automated; whether a decision can be reversed; and whether an error can be corrected. The city’s privacy and legal staff should determine what local requirements apply.
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San José provides one municipal example: its report describes required technology procurement review, initial risk analysis of proposals, and impact assessment for proposals classified as mid- or high-risk. These are practices described for that city, not universal requirements. The San José governance report also illustrates why a screening stage can help route proposals to a level of review proportionate to their risks.
The NIST AI Risk Management Framework is voluntary guidance, not a substitute for local legal review. NIST describes it as intended to improve the ability to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI systems. See the AI RMF overview and the NIST AI Resource Center.
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Turn assessment findings into procurement conditions
Give suppliers a clear description of the intended use and ask them to document the data they require, their governance and safeguards, system limitations, evidence supporting accuracy claims, external dependencies, and ongoing support. Make the city’s expectations concrete enough to check during evaluation and operation.
For each identified risk, record its owner, proposed mitigation, remaining risk after mitigation, and the decision point at which the city will reconsider whether to proceed. The UK guidance recommends go/no-go points and reassessment when a system changes substantially. That makes the assessment iterative: early review informs procurement, while the final system and its actual data flows may require further scrutiny before approval.
Decide, disclose, and monitor after approval
Before deployment, decide whether mitigations reduce the remaining risks to a level the city is willing and able to manage. Confirm who is accountable for oversight, staff training, incident escalation, user feedback, and periodic checks. Approval is a decision to operate under defined conditions, not a reason to stop examining the system.
Plan to monitor actual performance and impacts after launch, including changes to the data, vendor, model, or use. NIST’s AI RMF Playbook provides lifecycle-oriented guidance. San José’s report describes publishing a public algorithm register for approved systems affecting the public and ongoing monitoring of high-risk systems; those are examples a city can consider, not requirements for every municipality.
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