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Start with risk, not a blanket rule
A system that helps sort routine correspondence does not call for the same controls as one that influences access to public benefits, policing, health services or other consequential decisions. Safeguards should reflect the potential harm, how much the system acts without human intervention, who is affected and the conditions in which it will operate.
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This risk-based approach is consistent with the OECD AI Principles, adopted in 2019 and updated in 2024, and with the EU AI Act’s risk-based framework. It also helps agencies avoid two poor extremes: treating every AI use as equally dangerous, or assuming a system is safe because it is described as an assistant rather than a decision-maker.
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What to require before an agency deploys AI
1. Inventory the system and assess its intended use
Before procurement or deployment, require the agency to document what the system is, who supplies it, what it is intended to do, what data it uses, who will be affected and how its output enters a government service or decision. The assessment should consider foreseeable errors and misuse as well as ordinary operation.
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At minimum, ask whether the system could affect health, safety, fundamental rights, privacy, fairness, security or the reliability of public administration. Require the agency to consider whether a non-AI alternative would meet the need, and whether the risks change in the real service context compared with the vendor’s demonstration or test environment.
Reassess when the model, data, purpose, workflow or affected population changes. This assessment format is a practical policy recommendation grounded in lifecycle risk management; it is not a universal legal form required by every framework.
2. Set data and performance controls
Require documented data governance suited to the use. It should cover where data came from, whether it is suitable for the stated purpose, quality and privacy checks, security controls, and whether the data adequately represents the people likely to be affected. Agencies should test for different error patterns across relevant groups rather than rely on one overall performance figure.
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Before use, test the system under conditions resembling the intended operating environment and define acceptable performance thresholds. Record known limitations and uncertainty. Do not allow a supplier or agency to make accuracy claims that are not supported by evidence for the actual task and context.
The European Commission’s AI Act overview lists data quality, accuracy, robustness and cybersecurity among requirements for high-risk systems. Which obligations apply depends on the system’s legal category and the applicable dates; the overview should not be read as making every AI system subject to the same requirements.
3. Make human oversight effective
For consequential uses, a human reviewer needs more than a nominal place in the workflow. The agency should provide the reviewer with relevant information about the system’s limits, enough time and training to assess its output, and authority to reject or override it. Procedures should help reviewers notice anomalies, unexpected performance and changes in circumstances, while accounting for automation bias—the tendency to defer to a machine-generated result.
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Preserve a genuine human decision path where the consequences warrant it, with a clear escalation route for uncertain or disputed cases. Article 14 of the EU AI Act addresses human oversight of high-risk systems, but the European Commission AI Act Service Desk warns that its displayed article text has not been updated to reflect Digital Omnibus amendments. Consult the current consolidated law before relying on that page as the operative text.
4. Tell people when AI matters and let them seek review
When AI materially contributes to a service or decision, explain its role in a form suited to the interaction. Give staff and affected people useful information about relevant capabilities and limitations, and explain how to request review when appropriate. A person should have a practical route to challenge an output and seek human reconsideration of a consequential result.
The goal is actionable transparency, not a promise to reveal every technical detail or provide a complete explanation in every case. The OECD Recommendation on Artificial Intelligence calls for information that enables people adversely affected by an AI system to challenge its output; the precise notice and remedy required in a particular case depend on applicable law.
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Keep evidence, assign responsibility and prepare to respond
5. Maintain records that support investigation
Require records adequate to reconstruct what happened: the model and version in use, relevant input or data context, output, human actions, resulting decision and later system changes. Set retention and access rules that also respect privacy and other applicable obligations. The exact record schema is a policy choice, but records should be sufficient to investigate a complaint, failure or suspected rights impact.
Name accountable officials and define responsibility across procurement, deployment, monitoring and incident response. Agencies should have procedures for recording incidents, assessing their impact, notifying oversight authorities and affected people where required, correcting errors, and deciding whether use must be paused or withdrawn.
6. Monitor in operation and define stop-use conditions
Approval before launch is not a substitute for monitoring. Require periodic and event-triggered review for performance drift, changed data, new failure patterns, cybersecurity events, complaints and disparate effects. Independent review is especially useful for high-impact systems where it is feasible.
Set clear triggers for suspending use and require a workable route to roll back, repair or safely decommission a system. The OECD Recommendation says mechanisms should be in place, as appropriate, so systems that risk undue harm or exhibit undesired behaviour can be “overridden, repaired, and/or decommissioned safely as needed.”
7. Put enforceable safeguards into procurement
Government contracts should support the controls an agency is expected to operate. Address access to documentation, notice of incidents and material changes, cooperation with audits, cybersecurity support, and the allocation of responsibilities among the provider, integrator and government deployer. An agency also needs staff skills, governance ownership, suitable data infrastructure and procurement capacity to enforce those terms.
The OECD’s 2025 report on AI in core government functions groups measures into enablers, guardrails and engagement. Its topics include governance, data, digital infrastructure, skills, investment, procurement, transparency, risk management and oversight—an indication that safeguards depend on institutional capacity as well as model-level controls.
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| Framework | What it is | How to interpret it |
|---|---|---|
| EU AI Act, Regulation (EU) 2024/1689 | A binding EU regulation with risk-based obligations and phased application. | Its requirements depend on jurisdiction, system category, role and applicable dates. The European Commission overview says general-purpose AI governance rules and obligations became applicable on 2 August 2025, describes transparency rules as taking effect in August 2026, and includes a future-dated statement that high-risk obligations apply from 2 December 2027. Check current consolidated EUR-Lex text and Commission guidance for a particular system. |
| OECD AI Principles and Recommendation | International principles and a Council recommendation; the OECD AI Principles were adopted in 2019 and updated in 2024. | They offer guidance on risk management, oversight, traceability, transparency, accountability and safe response, but are not a single directly enforceable government statute. |
| NIST AI Risk Management Framework | A voluntary risk-management framework. | It can inform an agency’s risk practices but is not itself a binding legal requirement. NIST records the release of its Generative AI Profile, NIST-AI-600-1, on July 26, 2024. |
For any specific government system, check the rules that apply in its jurisdiction and sector, including privacy, procurement, administrative and equality requirements. The title alone does not establish a particular system’s legal classification, statutory impact-assessment duty or available remedy.
A practical test for a government AI policy
A policy is stronger when it can be checked and acted on, rather than merely stating broad principles. Before approving a system, decision-makers should be able to answer:
- What public task is the system meant to support, who could be affected, and what non-AI alternatives were considered?
- What evidence shows that its data and performance are suitable for the intended operating conditions?
- Who can understand, question and override its output, and how can an affected person request review?
- What records will make a decision and its system context traceable?
- Who is accountable for incidents, monitoring and corrective action?
- What conditions trigger a pause, repair, rollback or retirement?
The OECD AI Principles page reported more than 1,000 AI policy initiatives across more than 70 jurisdictions by May 2023. That is a count of initiatives recorded in the OECD.AI database—not a count of laws, successful programs or jurisdictions with equivalent safeguards. The number illustrates why governments should examine legal force and implementation, not simply count policies.
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