Evaluate an AI policy proposal by checking whether it turns its stated principles into clear duties: who must do what, for which systems and uses, using what evidence, and with what remedy if harm occurs. A practical review covers scope, proportionality, privacy, safety, fairness, transparency, human oversight, accountability, enforcement, and ongoing monitoring.
How should you define the proposal’s scope?
Start by describing the problem the proposal is meant to solve and the outcome it promises. Then identify the systems, organizations, sectors, uses, and stages of the AI lifecycle it covers. A policy that applies only to procurement, for example, may leave later deployment, updates, or retirement unaddressed.
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Map the people and institutions involved: providers, deployers, decision-makers, affected groups, oversight bodies, and anyone with authority to change or stop a system. Ask who can contest an outcome and where responsibility sits if the system contributes to harm. Also establish the governing jurisdiction; a proposal’s reach and legal obligations depend on where and how it applies.
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NIST’s AI Risk Management Framework (AI RMF) is a useful organizing method: Govern risk management, Map the context, Measure risks, and Manage them. NIST describes the framework as voluntary, so it can structure an evaluation but does not replace applicable law. NIST also says AI RMF 1.0 is being revised; check NIST’s current framework materials when applying it.
Is the proposed use necessary and proportionate?
For each covered use, look for a specific, legitimate objective and a clear connection between that objective and the AI system. The proposal should constrain the system to what is needed, rather than treating a broad goal as permission for any use of AI.
Ask whether a less intrusive or less risky method could achieve the same aim. UNESCO’s Recommendation on the Ethics of Artificial Intelligence calls for AI use not to go beyond what is necessary for a legitimate aim, and for risk assessment to help prevent resulting harms. A proposal is harder to justify when it does not explain why the intervention is needed or how its boundaries prevent unnecessary impacts.
Does it protect privacy throughout the AI lifecycle?
Privacy is not just a question of whether data collection is permitted. Check whether the proposal governs the data lifecycle from source to deletion, and whether it accounts for personal or sensitive information.
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- Data handling: Are collection, purpose of use, access, sharing, retention, security, and deletion addressed?
- Stewardship: Is a person or organization responsible for data governance and privacy-risk assessment?
- Rights and recourse: Can people find out how relevant data is used and exercise applicable rights?
- Representativeness: Does the policy address whether datasets are suitable for the people and contexts affected, while respecting privacy and data protection?
UNESCO says privacy should be protected and promoted throughout the AI lifecycle, alongside adequate data-protection frameworks. OECD’s AI principles likewise include privacy in ongoing lifecycle risk management and encourage governments to consider investment in representative open datasets that respect privacy and data protection.
How does it address safety, security, and misuse?
A robust proposal describes a continuing process to identify, assess, mitigate, and monitor risks—not merely a one-time approval. Check whether the assessment accounts for likelihood, duration, scope, and severity of possible harm, as well as changes in the system, its operating context, or available evidence. NIST notes that AI risks can be short- or long-term, localized or systemic, and high- or low-impact.
Consider more than intended, normal operation. OECD’s AI principles call for systems to remain robust, secure, and safe across their lifecycle, including under foreseeable use or misuse and adverse conditions. The policy should address failures and vulnerabilities, incident response, and ways to intervene when behavior becomes unsafe.
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- Can responsible staff override or pause the system in practice?
- Are there procedures to repair a system or safely decommission it if it poses undue risk?
- Does the policy require monitoring after deployment and reassessment when risks change?
NIST identifies safety; security and resilience; validity and reliability; privacy enhancement; accountability and transparency; explainability and interpretability; and fairness, with harmful bias managed, as characteristics of trustworthy AI. These are useful dimensions for checking what the policy covers and what it leaves unresolved.
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Look for explicit responsibilities assigned to the policy owner, system provider, deployer, auditors, and other relevant actors. Duties should match each actor’s role and practical ability to influence the system. A statement that an organization will “use AI responsibly” is difficult to verify unless it specifies who is responsible and what they must do.
Check what evidence must be kept, who may examine it, and how oversight works. Useful mechanisms include records and documentation, traceability for datasets, processes, and decisions, impact assessment, audit or due diligence, and a process for reviewing incidents. UNESCO calls for AI systems to be auditable and traceable and for oversight and assessment mechanisms; OECD also emphasizes traceability and systematic lifecycle risk management.
Accountability should include a response when a policy or system fails. Does the proposal provide a way to correct an outcome, investigate a complaint, suspend a harmful use, or end it? Can affected people contest decisions and receive a meaningful review? If independent review is absent, establish whether another effective oversight route exists.
Does transparency help without creating new risks?
People affected by AI and oversight bodies need enough information to understand how a system is used and to challenge relevant decisions. Look for requirements to explain the system’s role, document important decisions, and disclose information to the audiences who need it.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can you compare two or more proposals?
Use the same questions for each proposal, and distinguish a principle from an enforceable mechanism. Record the text that creates the duty, the actor responsible, the evidence that would show compliance, and the consequence or remedy if the duty is breached.
| Dimension | What to examine |
|---|---|
| Purpose and proportionality | Specific legitimate aim, connection to the AI use, and limits on unnecessary or more intrusive use. |
| Privacy and data governance | Data sources and handling across the lifecycle, assigned stewardship, privacy-risk assessment, and routes to exercise rights. |
| Safety and security | Foreseeable harms, misuse, vulnerabilities, mitigation, incident response, and the ability to override, repair, or stop a system. |
| Affected groups and fairness | Assessment of differential impacts, discrimination risks, and meaningful participation by affected people. |
| Transparency and explanation | Whether affected people and oversight bodies can understand relevant uses and challenge decisions, with suitable privacy and security protections. |
| Human oversight | Whether a responsible person has the authority, information, and practical ability to intervene. |
| Accountability and enforcement | Named responsibilities, required records, review or audit, remedies, and consequences. |
| Adaptability | Ongoing monitoring and a process to revise controls as systems, contexts, or evidence change. |
These dimensions draw on NIST’s risk and trustworthiness concepts, UNESCO’s human-rights principles, and OECD’s lifecycle and accountability principles. If a proposal states an aspiration but does not identify a responsible actor, evidence, or mechanism, mark that as an implementation gap rather than assuming the promise is enforceable.
Which legal requirements apply?
Do not treat a general AI policy checklist as a legal determination. Duties depend on the jurisdiction, the system and use, the organizations’ roles, and the applicable dates. Check the current official legal text and relevant regulator guidance before concluding that a proposal meets the law.
The EU AI Act is an example of a regional, risk-based legal framework, not a universal checklist. The European Commission’s overview lists measures for high-risk AI that include risk assessment and mitigation, data quality, logging, documentation, human oversight, robustness, cybersecurity, and accuracy, along with monitoring and incident-reporting roles. The Commission page states that the Act became applicable on 2 August 2026 subject to exceptions and describes extended transition dates for specified high-risk uses following the 2026 AI Omnibus. Check the current rules for the specific system and use before relying on that general date.
The AI Act Service Desk’s Article 27 summary says certain public bodies and private entities must conduct a fundamental-rights impact assessment before deploying specified high-risk systems. It describes the assessment as addressing the system’s use, affected groups, risks, human oversight, and mitigation. It also notes that relevant sections may be cross-referenced where an applicable data-protection impact assessment already meets the obligations. Whether this applies to a particular deployment depends on the Act’s scope and circumstances.
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