Before using AI, governments should require safeguards that match the system’s risks and continue from procurement through deployment and retirement. At a minimum, agencies need to assess risks before use, test data and performance, provide meaningful human oversight, tell affected people how AI contributes to decisions, keep records, monitor outcomes, and be able to correct or stop unsafe systems. The exact legal duties depend on the jurisdiction and use case.
What should safeguards accomplish?
A safeguard is useful only if it changes what an agency can detect or do. It should help prevent foreseeable harm, reveal when a system is failing, make responsibility traceable, and provide a practical response—such as review, correction, suspension, or safe withdrawal.
Controls should scale with the system’s potential impact, degree of autonomy, and operating context. A tool that helps staff draft routine text does not call for the same controls as one that influences access to public benefits, policing, health services, or other consequential decisions. Risk management should also account for the agency’s role: a government buying a system, integrating it, or operating it may have different responsibilities from its provider.
The OECD’s AI Principles, adopted in 2019 and updated in 2024, recommend lifecycle risk management adapted to roles and context. The EU AI Act is a binding regulation within its scope and uses a risk-based framework. These instruments are not interchangeable: an OECD recommendation is guidance, not by itself a directly enforceable statutory duty.
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What should an agency require before procurement or deployment?
1. Inventory the system and assess its use in context
Before a purchase or launch, require the agency to document what system it will use, who supplies it, its intended purpose, the people it may affect, the data it processes, and whether it advises, ranks, recommends, or makes decisions. Assess foreseeable risks to health, safety, fundamental rights, privacy, fairness, security, and public administration.
The assessment should consider likely failures and misuse, whether a non-AI alternative would work, and how the agency’s particular workflow changes the risk. Revisit it when the model, data, purpose, process, or affected population changes. This is a practical baseline drawn from risk-based and lifecycle governance principles; it is not one universal assessment form mandated across all jurisdictions.
2. Govern data and verify performance
Require evidence that the data are suitable for the intended use, with documented provenance, quality checks, privacy and security controls, and attention to representation and error patterns among affected groups. Agencies should set performance thresholds before deployment and test the system under realistic operating conditions, not just rely on a supplier’s general claims.
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Documentation should describe limitations and uncertainty. Do not make accuracy claims that exceed the available evidence. The European Commission’s AI Act overview identifies data quality, accuracy, robustness, and cybersecurity among requirements for high-risk systems; whether those particular legal requirements apply depends on the system’s classification and the law’s applicable dates.
3. Set clear standards for oversight and intervention
Assign oversight to people who have the authority, time, training, and information needed to understand relevant limitations, notice anomalies, interpret outputs, and reject or override them. A reviewer’s name on a workflow is not meaningful oversight if staff must accept the output, cannot inspect enough context, or have no workable escalation path.
For consequential decisions, preserve a genuine route to human judgment and document how staff should handle uncertain, unexpected, or disputed results. Train reviewers to recognize automation bias—the tendency to defer to a system simply because it produced a recommendation—and define who can pause use when circumstances or performance change.
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Article 14 of the EU AI Act addresses human oversight of high-risk systems. However, the European Commission AI Act Service Desk’s displayed Article 14 page states that its text has not been updated to reflect Digital Omnibus amendments. Consult the current consolidated law before relying on that page as the operative text.
What information and recourse should people receive?
People should be told when AI materially contributes to a service or decision, using notice suited to the setting. Staff and affected people also need meaningful information about the system’s role and important limitations—not a promise of a complete technical explanation where that is not feasible or required.
When an AI-supported output could adversely affect someone, provide a practical way to ask questions, challenge the result, and obtain human reconsideration where appropriate. The OECD Recommendation on Artificial Intelligence calls for information that enables people adversely affected by an AI system to challenge its output. Notice without a usable review channel is not an adequate remedy.
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What records and accountability should be required?
Agencies should be able to reconstruct how a consequential output became part of a decision. Require records that, subject to privacy and retention rules, capture the model and version, relevant input or data context, output, human actions, resulting decision, and subsequent system changes.
Name officials responsible for procurement, deployment, monitoring, incident response, and any required public reporting. Establish procedures to log and assess incidents, notify the appropriate oversight authority and affected people when required, correct errors, and pause or withdraw a system when necessary. The OECD principles identify traceability of datasets, processes, and decisions as a basis for accountability; the specific record fields above are a practical policy recommendation, not a universal legal schema.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should governments monitor and stop unsafe systems?
Governance must continue after launch. Set periodic and event-triggered checks for performance drift, changed data, new failure patterns, cybersecurity events, complaints, and unequal effects. For high-impact systems, independent review can add assurance where it is feasible.
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Define in advance what conditions trigger investigation or stop-use action, who has authority to act, and how the agency can roll back, repair, or safely decommission the 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, or safely decommissioned.
How should procurement make safeguards enforceable?
Contract terms should give the government the cooperation it needs to carry out its duties. Depending on the system and applicable law, that can include access to documentation, incident notification, audit cooperation, notice of material changes, cybersecurity support, and a clear allocation of responsibilities among the provider, integrator, and government deployer.
Contract language alone is not enough. Agencies need accountable governance owners, skilled staff, suitable data and digital infrastructure, and procurement capacity to verify compliance and respond when a supplier does not cooperate. The OECD’s 2025 report on AI in core government functions groups trustworthy-AI measures into enablers, guardrails, and engagement, including governance, data, infrastructure, skills, procurement, transparency, risk management, and oversight.
How do the main frameworks differ?
| Framework | Legal force and reach | What it contributes | Timing or qualification |
|---|---|---|---|
| EU AI Act (Regulation (EU) 2024/1689) | Binding regulation within its EU scope | Risk-based obligations, including requirements for high-risk systems and roles for providers, deployers, and public authorities | Obligations apply in phases. The Commission overview lists general-purpose AI governance rules as applicable from 2 August 2025 and transparency rules from August 2026; it also contains an implementation update referring to high-risk obligations from 2 December 2027. Check the current consolidated law and Commission guidance for the relevant system and date. |
| OECD AI Principles and Recommendation | Recommendations and guidance, not a single directly enforceable government statute | Lifecycle risk management, transparency, human oversight, traceability, accountability, and mechanisms to override, repair, or decommission harmful systems | Principles adopted in 2019 and updated in 2024; legal duties may arise from other laws that implement or overlap with them. |
| NIST AI Risk Management Framework | Voluntary risk-management framework | A structured resource for organizing AI risk management | NIST records the release of its Generative AI Profile, NIST-AI-600-1, on July 26, 2024. |
When comparing a proposed safeguard or framework, check its jurisdiction and legal force, which risks and uses it covers, whether it spans the lifecycle, what review and remedies affected people can access, what evidence regulators can inspect, and whether agencies have the staff and vendor cooperation to make it work.
For context, the OECD AI Principles page reports more than 1,000 AI policy initiatives across over 70 jurisdictions by May 2023. That figure counts reported initiatives in the OECD.AI database; it is not a count of laws, successful programs, or jurisdictions with equivalent protections.
What can be concluded without a specific country or use case?
A practical baseline is clear: require documented risk assessment, evidence-based testing, real human authority, useful notice and challenge routes, traceable records, ongoing monitoring, and a safe way to intervene. The precise classification of a system, impact-assessment duties, procurement and privacy rules, and available remedies cannot be determined without the jurisdiction and the actual government use. Agencies should check the law and regulator guidance that apply to their system before deployment.
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