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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesLocal governments can reduce AI risks by treating each system as a public-service tool that must be justified, assessed, tested, governed, and monitored throughout its lifecycle. Start by asking whether AI is needed at all; then match safeguards to the potential impact, protect data, require meaningful human oversight where decisions affect people, and establish a way to correct, pause, or retire the system. The NIST AI Risk Management Framework (AI RMF) offers voluntary guidance—not a legal compliance certificate—and specific legal duties depend on the locality and use.
Start with the public problem, not the AI product
Define the problem the local government is trying to solve and the result it expects. Compare an AI system with a simpler process, such as clearer forms, improved staffing, or ordinary automation. Consider what happens if the system is wrong, unavailable, or used beyond its intended purpose. NIST says deciding whether AI is appropriate or necessary is a shared responsibility that should account for context, risks, impacts, costs, benefits, and interested parties. See the NIST AI Risk Management Framework 1.0.
A useful first distinction is how much a system can affect a resident and how readily a mistake can be reversed. A drafting assistant and a tool that influences access to benefits should not receive the same level of scrutiny.
| Proposed use | Key risk questions | Practical safeguard emphasis |
|---|---|---|
| Drafting or summarizing internal material | Could staff mistake an inaccurate summary for an authoritative record? Does the input contain confidential or personal information? | Approve the tool for the data involved, verify material outputs against source documents, and prohibit unreviewed use in consequential decisions. |
| Supporting decisions about benefits, enforcement, hiring, or access to services | Could an error or uneven performance harm a person? Can the affected person understand, challenge, and correct the result? | Conduct a fuller impact assessment, test relevant local workflows and populations, require meaningful human review, and provide a clear escalation and correction route. |
These are illustrative distinctions, not legal risk categories. The appropriate review depends on the system, its purpose, the people affected, and local requirements.
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Assess the use and its likely impacts before deployment
Before buying or launching a system, document what it will do and what it will not do. Include the intended users, affected communities, data inputs and their provenance, outputs, dependencies, known limitations, and foreseeable misuse. Assess privacy, security, discrimination, accessibility, reliability, and other harms specific to the setting. The NIST AI RMF Core and its Govern Playbook describe lifecycle risk management and governance practices; the Playbook also cautions that legal and regulatory obligations vary by application and context.
Scale the review to the potential effect and reversibility of an error. A system that handles sensitive data, operates with little human review, or affects access to services needs closer scrutiny than a low-impact internal aid. Revisit the assessment if the system, its data, or its use changes.
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Assign accountable owners and operating rules
Maintain an inventory of AI systems, including their purpose, vendor, data involved, accountable owner, and review status. Name an executive accountable for oversight and an operational owner responsible for day-to-day use. Set permitted and prohibited uses, staff training requirements, review frequency, and an exit plan. Staff should know who can approve use, override an output, escalate a concern, and pause the system.
Governance is ongoing, not a one-time approval. NIST’s Govern Playbook describes assigning lifecycle responsibilities, reviewing systems, training people, and planning for safe decommissioning.
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Make procurement a risk-control point
A local government remains responsible for evaluating a third-party system in its own context. Before purchase, request enough information to assess the system’s function, limitations, data provenance and handling, testing, security, and ability to support audits. Where feasible, contract for reporting and audit rights, notice of significant system changes, incident reporting, and clear limits on vendor retention or reuse of government data. Plan how essential functions will continue if a vendor fails or the service must be stopped.
These are procurement safeguards, not a universal contract template. Exact terms require local procurement and legal review. NIST’s AI Government Procurement Guidelines and Generative AI Profile discuss procurement and third-party risk practices.
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Test in local conditions, then keep monitoring
Do not treat a vendor benchmark or general performance claim as proof that a system works for a particular municipality. Before launch, test representative local workflows and relevant populations. Examine error patterns and disparate effects, document the limits and thresholds, and record unresolved risks and the decision to proceed. NIST’s Generative AI Profile notes that pre-deployment testing can be inadequate or mismatched to real-world use; it recommends iterative, documented testing and evaluation.
After launch, monitor output quality, complaints, incidents, unexpected uses, and signs that performance has changed. Set in advance what findings trigger additional review, human escalation, rollback, suspension, or reassessment. The NIST Generative AI Profile recommends applying and documenting testing, evaluation, validation, and verification iteratively across the lifecycle.
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Keep human review meaningful for consequential uses
For decisions that can materially affect a person, a human reviewer needs the authority, time, and understanding to verify important outputs, reject a recommendation, or escalate a case. Train reviewers on system limitations and define what they must check. A nominal human-in-the-loop step is not meaningful if staff cannot influence the outcome.
Give affected people an accessible way to ask questions, seek review, or request correction, consistent with applicable local law. Keep records that allow the government to understand how an output was used and who reviewed it. NIST’s AI RMF says: “Human judgment should be employed when deciding on the specific metrics related to AI trustworthiness characteristics and the precise threshold values for those metrics.”
Protect data and explain the use appropriately
- Minimize sensitive information supplied to a system and restrict access to approved users.
- Set retention rules and assess how a vendor stores, accesses, and may reuse government data.
- Do not enter confidential or personal information into tools that have not been approved for that data.
- Explain where AI is used when appropriate, why the use is justified, and how a person can request review or correction.
- Keep records needed for oversight and public accountability, subject to applicable law.
Privacy, fairness, accuracy, security, and explainability can conflict. Make tradeoffs explicit in the actual decision context and consider input from affected people and relevant stakeholders rather than optimizing one measure in isolation. The NIST AI RMF 1.0 treats these trustworthiness characteristics as contextual considerations.
Use the NIST framework as guidance, alongside local law
The AI RMF is voluntary guidance for managing risk; using it does not establish that a government has met every legal duty. Requirements may depend on the jurisdiction, the service, the data, and how the system affects people. Local governments should have appropriate counsel review specific legal obligations before adopting or expanding a use. NIST’s framework status can also change; consult NIST’s current AI RMF materials when applying it.
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