Assess a proposed AI tool before procurement, then keep reviewing it throughout its use. A useful assessment ends in a recorded decision: proceed, proceed with safeguards, run a limited pilot, redesign or restrict the use, or reject it. NIST’s AI Risk Management Framework (AI RMF) offers voluntary, lifecycle-oriented guidance; it does not replace legal review of the rules that apply in your city and use case.
What a city AI risk assessment should decide
The assessment is not simply a technical check or a form to complete. It should connect the proposed public benefit to the people, data, decisions and operational conditions involved, identify safeguards for material risks, and make clear who owns the remaining risks. NIST says its framework is intended to help developers, users and evaluators manage risks that could affect individuals, organizations, society or the environment. The AI RMF 1.0 was released on January 26, 2023, is voluntary, and NIST says it is being revised; check the current AI RMF page for its status.
Use the result to decide whether to proceed as proposed, add conditions, test through a limited pilot, narrow or redesign the use, or stop. A risk assessment should begin before a purchase commitment and continue through deployment, operation and evaluation. NIST’s AI RMF Playbook is supporting guidance, not a mandatory sequence: NIST explicitly says it is neither a checklist nor a set of steps that must all be followed.
1. Define the proposed use and its boundaries
Describe the service problem before discussing a vendor or model. A clear use description lets reviewers assess whether AI is suitable for the task and what could happen if the system is wrong, unavailable or used outside its intended purpose. NIST’s AI RMF treats understanding context as part of lifecycle risk management.
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- Service and intended benefit: What public-service problem is the city addressing, and what measurable improvement is expected?
- Task and decision: What will the system generate, classify, recommend or automate? Will it inform a decision or make one?
- People affected: Which residents, workers or other groups may be affected, including people who do not directly use the tool?
- System and workflow: Identify the model, vendor services, integrations, data sources and staff actions that make up the system. Include third-party or generative AI components.
- Failure and fallback: What happens if the tool produces an inaccurate result, becomes unavailable, or is used in a foreseeable but unintended way? Can staff continue the service without it?
2. Assign owners and review gates before procurement
Name a business owner who is accountable for the service outcome, not just the technology purchase. Identify the officials who must review the proposal and when their approval is needed. Depending on the use, that can include technology, procurement, privacy, information security, legal, accessibility, records management and equity staff.
Portland offers a concrete municipal example: its BTS-4.04 Artificial Intelligence Use and Governance policy requires a requestor to submit a business case to the Bureau of Technology Services for an initial risk assessment before initiating AI procurement. The policy also identifies privacy, equity and surveillance reviews that may be coordinated as applicable. That is Portland’s process, not a rule that automatically applies to other cities.
3. Map data, people and possible harms
Trace what information enters the system, what it produces, where data travels and who can access it. For a hosted or generative AI service, do not assume that a vendor’s general privacy statement answers the city’s specific questions. NIST’s Generative AI Profile, published July 26, 2024, highlights privacy, information security, third-party transparency and impact assessment as relevant risk areas.
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- Where is it stored or processed, which subcontractors handle it, and which city or vendor personnel can access it?
- How long does the supplier retain prompts, outputs, logs and uploaded files? Can the city obtain deletion, and how is deletion confirmed?
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For generative AI, assess the full workflow rather than the model in isolation: a prompt may contain sensitive information, an output may be wrong or disclose information, and an integration may send data to another service. The federal Government of Canada’s Guide on the use of generative artificial intelligence directs federal institutions to consult privacy officials to determine whether a Privacy Impact Assessment is required. That is Canadian federal guidance, not a universal requirement for every city; ask your jurisdiction’s privacy officials which assessments apply.
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4. Judge consequences, safeguards and resident recourse
Risk depends on the use, not just the tool’s technical sophistication. Consider who could be harmed, how serious the harm could be, how many people could be affected, and whether it can be reversed. Give heightened scrutiny to uses that could affect rights, health, safety, access to public services or finances.
NIST’s AI RMF FAQs identify trustworthiness characteristics to consider: validity and reliability; safety; security and resilience; accountability and transparency; explainability and interpretability; privacy; and fairness, with harmful bias managed. Translate those characteristics into use-specific questions rather than treating them as a universal score.
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- When does a qualified person review the output, and can that person reject it rather than merely approve it?
- Can a resident receive a meaningful explanation, request human review, contest an outcome or report a problem?
- Who investigates a complaint or error, corrects records and communicates a remedy?
- What controls prevent staff from treating a probabilistic recommendation or generated answer as verified fact?
- Is the proposed use appropriate at all if effective human review or resident recourse cannot be provided?
Portland’s policy specifically flags consequential decisions made without an appropriate level of human review as a concern. The appropriate level depends on the stakes and the actual workflow; a nominal human sign-off is not meaningful oversight if the reviewer lacks time, context or authority to disagree.
5. Test the system before launch
Set the intended operating conditions and acceptance criteria before evaluating results. Test realistic cases, edge cases and foreseeable misuse, not only examples selected by a supplier. NIST’s Generative AI Profile recommends iterative, documented testing, evaluation, validation and verification early in the generative AI lifecycle.
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- Check performance across relevant groups and circumstances; document where the test set is incomplete or results cannot support a conclusion.
- Test privacy and security controls, including integrations, access restrictions and handling of sensitive inputs.
- Exercise the human workflow: confirm staff can recognize uncertainty, escalate a problem and override or reject an output.
- Record test methods, data and scenarios, limitations, results, reviewers and unresolved issues.
A successful test does not prove the tool will remain reliable after launch or after a vendor changes the model. Use the findings to set operating limits and monitoring needs, and do not treat untested claims as established performance.
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6. Review suppliers and contract terms
Ask the supplier for technical documentation about data handling, model behavior, limitations, integrations and any adaptive or learning components. Portland’s policy calls for a hosted-service questionnaire and AI-specific vendor disclosures, including whether city data is used for training or improvement. NIST’s Generative AI Profile discusses acquisition due diligence and possible third-party controls such as service-level and assurance documentation.
Make the city’s requirements explicit in contract language where applicable. Review terms covering:
- Permitted uses, data ownership, retention, deletion and any training or product-improvement use.
- Incident notification, cooperation in investigation and responsibility for response.
- Access to audit, evaluation or assurance materials needed to verify controls.
- Notice and review rights for material model, service, data-processing or subcontractor changes.
- Subcontractors, security responsibilities, service levels and operational support.
- Export of city data, transition assistance and an exit path if the service is unsuitable or the vendor relationship ends.
Do not assume that a vendor questionnaire alone resolves risk: compare its answers with the proposed workflow, test evidence and the terms the city can enforce.
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7. Compare options and record a decision
If the city is choosing among tools or designs, compare them against the same use-specific criteria. NIST and government procurement guidance inform these dimensions, but they are a practical comparison framework, not a NIST scoring scheme.
| Comparison dimension | Question for the city |
|---|---|
| Public benefit and task fit | Does this option address the service problem, and is AI necessary for the task? |
| Potential harm | How severe, widespread and reversible could a failure or misuse be? |
| Data and vendor use | How sensitive is the data, how long is it retained, and can the supplier reuse it? |
| Reliability | What performance has been tested across relevant conditions and groups, and what remains unknown? |
| Transparency and auditability | Can the city and affected people understand or audit the system’s role and limitations? |
| Human review and recourse | Can staff intervene effectively, and can residents seek review or report harm? |
| Lifecycle viability | Can the city operate and oversee the system, manage vendor dependence and exit if needed? |
Document the expected benefit, affected people, assessed impacts, evidence, residual risks, safeguards, responsible owners, approval conditions and reasons for the outcome. NIST’s Govern Playbook describes impact assessments as a way to document impacts and support oversight, with reassessment as goals and outcomes evolve.
8. Monitor, respond and reassess during operation
Before launch, define what the city will monitor, who reviews it and what triggers action. A practical monitoring plan can track errors, complaints, incidents, changed vendor behavior, model or data drift, and different outcomes across relevant groups. Name an owner with authority to pause or roll back use, and establish how staff will report issues and how affected residents can raise concerns.
Reassess after a material change to the model, data, purpose, integration or affected population, and when operational evidence shows that assumptions no longer hold. This lifecycle approach is consistent with NIST’s emphasis on context-specific, iterative risk management; it is not a verbatim mandatory NIST checklist. The NIST AI RMF Playbook also says its online guidance will be updated after the framework revision.
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Requirements depend on jurisdiction, service and system. The voluntary NIST framework does not establish whether a particular city must conduct a privacy, civil-rights or other impact assessment, nor does it replace applicable procurement, records, accessibility, security or legal obligations. Before approving a use, ask your city’s legal, privacy, security, procurement, accessibility and records officials to identify the binding rules and review gates that apply.
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