To evaluate an AI policy proposal, ask what specific public problem it addresses, how its rules are expected to help, what realistic alternatives exist, and who gains or bears the costs and risks. Then check whether the responsible institutions can implement it and whether outcomes will be measured and reviewed. That approach lets you judge the proposal on evidence and trade-offs rather than whether it is labelled “pro-AI” or “anti-AI.”
Start by asking what success would look like
Before debating whether a proposal is too strict or too permissive, identify its intended outcome. Is it meant to reduce a particular harm, protect a right, address a market failure, or meet another public need? A useful objective is specific enough that people can later assess whether it was achieved.
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Next, check whether the evidence supports the problem’s stated scope. Evidence that an AI system can cause harm—or that a particular incident occurred—does not by itself show that a proposed law will prevent it. The proposal needs a plausible account of how its mechanism connects to its objective.
- Problem: What is happening, to whom, and in which settings?
- Objective: What outcome is the policy trying to change?
- Mechanism: How are its requirements expected to produce that change?
- Evidence: What supports the problem assessment and the proposed mechanism?
The OECD’s regulatory-policy guidance calls for defining the policy need and objective, and says that when proposals would have significant impacts, ex ante assessment of costs, benefits and risks should be quantitative whenever possible. Quantification is not a substitute for explaining what the numbers mean or what they leave out. OECD guidance on regulatory impact assessment
Compare the proposal with realistic alternatives
A proposal should not be judged against an imaginary world with no safeguards or response. Include the status quo, or no action, as a baseline, then compare plausible alternatives. Depending on the problem, alternatives might include education, voluntary standards, non-regulatory measures, regulation, or a combination.
The OECD identifies core elements of regulatory impact assessment as defining the problem and objective; describing the proposal; identifying alternatives; analyzing benefits and costs; selecting a preferred solution; and setting out monitoring and evaluation. OECD regulatory impact assessment principles
For each option, use the same questions rather than changing the test to favour one side:
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- How effectively is it expected to address the defined problem?
- What are its effects on rights, safety, security, privacy and fairness?
- How are benefits and burdens distributed among people and organizations?
- What direct, indirect and opportunity costs could result?
- Can the responsible institutions enforce it in practice?
- What might it mean for innovation, competition and consumer welfare?
- How will results be monitored, reviewed and adjusted?
Account for benefits, costs and who bears them
Look beyond the proposal’s headline cost or promised benefit. An assessment should consider direct and indirect effects, opportunity costs, and economic, social and environmental impacts. It should also ask who experiences each effect and when: a policy may benefit one group while imposing costs or risks on another.
Use credible quantitative estimates for significant impacts when evidence allows. But rights, fairness and distributional consequences can be difficult to reduce to a single number. Describe them clearly rather than implying that an aggregate estimate captures everyone’s experience. A cost figure alone is not a complete assessment if it leaves out benefits, indirect effects, rights impacts or how burdens are shared.
Ask which AI systems and contexts the proposal covers
“AI” is not one technology or one risk category. A policy may affect systems differently depending on who uses them, what they do, what data they rely on and where their outputs are used. The OECD framework for classifying AI systems organizes relevant context into five dimensions:
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- People and planet: who may be affected, and what environmental context matters.
- Economic context: the sector and conditions in which the system is used.
- Data and input: the information or inputs the system processes.
- AI model: the model and its relevant properties.
- Task and output: what the system does and how its results are used.
These dimensions help test whether a proposal’s scope matches the settings and systems behind the stated problem. They also help reveal when a broad rule might reach beyond the cases it is intended to address. OECD framework for the classification of AI systems
Then consider the proposal’s effects on human rights and democratic values, harmful bias, safety, security and privacy, labour and intellectual-property rights, accountability and ongoing risk management. Its effects on innovation and competition matter too. These questions should be considered together: for example, a requirement intended to reduce one risk may affect access, competition or the ability to challenge a consequential decision.
Check whether institutions can carry it out
A policy can have a coherent objective and still fail if no institution has the authority, expertise, funding or staff to administer it. Identify which agencies or other bodies must act, what information they need, and how oversight and compliance will work.
- Which institution is responsible for each duty?
- Does it have the technical, financial and human resources required?
- What information will it need from developers, deployers or affected people?
- How will compliance be checked, and what happens when requirements are not met?
- Can the policy be monitored for unintended effects and revised if it is not working?
Monitoring should start with a baseline and indicators tied to the policy’s objective. Specify when results will be reviewed and how evidence will inform changes. A technical test of a model can contribute evidence about an AI application, but it does not establish whether a policy improves social outcomes or whether agencies can enforce it.
NIST’s ARIA Evaluation Planning Manual, published September 18, 2026, describes a holistic AI evaluation approach combining Model Testing, Red Teaming and User Testing. Those methods concern evaluation of AI applications; policy assessment also needs institutional, social and distributional analysis. NIST ARIA Evaluation Planning Manual
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The European Commission’s 2021 assessment
The European Commission published an impact assessment of its AI regulatory initiative on April 21, 2021. Its stated purpose was to assess the case for action, objectives and impacts of different policy options for a European AI framework. It is an example of comparing a case for action and policy options; it does not, by itself, prove that any option or later implementation succeeded. European Commission impact assessment of the AI regulatory framework
Review duties in the EU AI Act
Article 112 of the EU AI Act is an example of review provisions in a specific jurisdiction. The consolidated text displayed by EUR-Lex is based on a version dated July 27, 2026, alongside the official regulation dated June 13, 2024. It sets dates and recurring evaluations covering matters including risk categories, transparency, supervision and governance, enforcement, authority resources, penalties, standards, market entrants and SMEs, the AI Office, energy-efficient model standardisation and voluntary codes of conduct. It also calls for attention to technological developments and effects on health, safety and fundamental rights. These are EU-specific provisions; check the current law and applicable dates before relying on them. EU AI Act, including Article 112
Make the judgment—and its uncertainty—visible
Once the options have been compared, state which one best meets the objective and why. Name the evidence supporting that judgment, the important uncertainties, who stands to benefit and who may bear costs, and what safeguards or revisions could improve the result. This makes value judgments visible instead of hiding them in a political label or a single aggregate figure.
The OECD AI Principles describe their aim as promoting AI that is innovative and trustworthy and respects human rights and democratic values. The OECD’s 2023 page reported that, by May 2023, governments had reported over 1,000 policy initiatives across more than 70 jurisdictions in the OECD.AI national policy database that follow the Principles. That is a dated historical report, not a current count of all AI policy initiatives. OECD AI Principles
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Quick Recap
A checklist for evaluating an AI policy proposal
- Write down the specific problem and the outcome the proposal seeks.
- Check the evidence for the problem’s scope and the proposal’s causal logic.
- Compare it with the no-action baseline and plausible regulatory and non-regulatory alternatives.
- Assess effectiveness, rights, safety, fairness, distribution, costs and effects on innovation and competition.
- Identify which AI systems and use contexts are in scope.
- Check the responsible institutions’ authority, resources, information needs and enforcement plan.
- Look for a baseline, outcome indicators, review dates and a way to adapt the policy.
- Explain the preferred option, trade-offs and uncertainties in plain language.
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