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Assess a proposed AI policy by comparing it with a clearly defined no-action baseline and realistic alternatives. Define the public problem and objective, map how each requirement is expected to change behaviour, identify costs and benefits for businesses, citizens and government, quantify significant effects where evidence allows, and describe important impacts that cannot be monetised. Make distributional effects, uncertainty, implementation costs and enforcement capacity explicit. Finish with indicators and an evaluation plan that can detect whether the policy worked and support revisions.
There is no universal AI-policy estimate. The jurisdiction, affected sectors, legal rules and technical design determine the relevant evidence and compliance burden. Treat the method below as a decision framework, then tailor every assumption to the specific proposal.
What does an economic-impact assessment of an AI policy include?
An assessment is a structured comparison of the proposed intervention, what would probably happen without it, and feasible alternatives. It should explain the causal mechanism, estimate material effects over a stated time horizon, show who gains and who pays, and disclose uncertainty rather than presenting a single unsupported total.
Use the same baseline, affected population, time horizon and price basis for every option. A credible assessment normally covers:
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- the problem and policy objective;
- the AI activities and jurisdictions in scope;
- a business-as-usual or no-action scenario;
- regulatory, non-regulatory and combined alternatives;
- direct, indirect, opportunity, administrative, capital, enforcement and implementation costs;
- benefits and avoided harms;
- distribution across firms, workers, consumers, communities and government;
- uncertainty, evidence quality and sensitivity to key assumptions; and
- monitoring, evaluation and revision arrangements.
OECD regulatory-impact principles provide the overall assessment structure. UK guidance on evaluating AI interventions adds theory-of-change, comparison-group and iterative-evaluation techniques. The European Commission’s 2021 assessment of a proposed AI regulation illustrates how compliance activities can be translated into time and resource estimates, but its assumptions are proposal-specific.
How do I define the policy problem, objective and scope?
Start by stating the harm or missed opportunity the proposal addresses, the evidence that it exists, and the outcome the policy is intended to change. Then delimit the systems, uses, actors, geography and time period covered; without these boundaries, costs and benefits cannot be assigned reliably.
Describe the problem with observable evidence
Separate the underlying problem from a preferred solution. For example, a problem statement might concern demonstrable risks from a class of automated decisions, inadequate information for affected people, or barriers to safe adoption. Record the affected population, frequency or severity where known, and the consequences of leaving the problem unchanged. If evidence is incomplete, identify the uncertainty instead of implying that the harm is either universal or negligible.
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Write a measurable objective
Express the objective as a change in an outcome, such as reducing a specified harm, improving contestability, or increasing reliable compliance. Avoid objectives that merely restate an instrument (“require documentation”). A measurable objective lets you test effectiveness against alternatives and later evaluate whether the intervention worked.
Specify the AI-policy perimeter
State whether the proposal governs development, deployment, procurement, a particular use, or several stages. Identify covered providers, deployers, public bodies, downstream users and affected individuals. Note exemptions, thresholds, geographic reach and interactions with existing rules. The same requirement can have very different economic effects on a small developer, a large deployer and a public agency.
Map the causal pathway
Draw the chain from requirement to behaviour to outcome. A documentation rule, for instance, may lead an organization to allocate staff time, which may improve oversight, reduce harmful deployments and alter user trust. List assumptions at each link, including the ability of firms and regulators to comply, the availability of skilled personnel and the likelihood that actors adapt in other ways.
What should the no-action baseline contain?
The baseline is the most plausible description of what happens without the proposal, not a frozen world in which nothing changes. Include existing law, enforcement, voluntary standards, expected technology and market changes, and likely behaviour by firms, users and government.
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Build the baseline step by step
- Inventory current obligations. Record applicable sectoral, consumer-protection, privacy, safety, employment and procurement rules, including how they are actually enforced.
- Project normal change. Account for expected AI adoption, model capability, prices, workforce skills and business practices over the chosen horizon.
- Estimate ongoing harms and benefits. Describe the risks, productivity effects, access changes and public expenditures likely to occur without new action.
- Document baseline assumptions. Give sources, dates, geographic coverage, units and confidence levels so another analyst can reproduce the scenario.
Always include a no-action scenario, as OECD guidance recommends. If the baseline is uncertain, use more than one plausible baseline and show how the preferred option performs under each.
Which policy alternatives should I compare?
Compare the proposal with feasible approaches that could achieve the objective, including non-regulatory measures. Alternatives might differ in scope, obligations, timing, supervision or combinations of regulation with education, guidance or voluntary standards.
| Comparison axis | Questions to answer |
|---|---|
| Effectiveness | How much does each option change the defined problem relative to the baseline, and through which mechanism? |
| Total costs and benefits | What are the effects on businesses, citizens and government, including costs outside the regulator’s budget? |
| Distribution | Which sectors, firm sizes, income groups, workers, regions or generations bear costs or receive benefits, and when? |
| Evidence and uncertainty | How strong is the evidence, which assumptions drive the result, and what happens in sensitivity or scenario tests? |
| Implementation and adaptability | Can institutions enforce the option, monitor outcomes and revise requirements as AI systems and evidence change? |
Include a combined option when a rule is likely to work only with guidance, training, technical infrastructure or a complaints process. Explain trade-offs rather than ranking options on a single aggregate score.
How do I identify all economic costs and benefits?
Map impacts by actor and by time. Distinguish one-time changes needed to comply from recurring costs, and count opportunity costs when resources are diverted from other productive or socially valuable uses.
Businesses and other regulated organizations
- Staff time for risk assessment, testing, documentation, quality management, reporting and incident response.
- Technical changes, data work, security controls, audits, legal advice, training and external assurance.
- Capital expenditure for systems, tools or specialist personnel, with any ongoing maintenance.
- Delays, reduced product scope, foregone deployments or innovation displaced by compliance work.
- Potential benefits such as fewer incidents, clearer requirements, improved customer trust or access to markets.
Citizens, workers and consumers
- Changes in prices, service availability, waiting times, employment, skills demand and privacy or safety.
- Benefits from avoided harm, more accurate or contestable decisions, and better information.
- Distributional effects on people who are especially exposed to automated decisions or have less ability to switch providers.
Government and public institutions
- Rulemaking, guidance, registration, supervision, inspections, investigations, sanctions, courts and appeals.
- Procurement, technical infrastructure, training and data collection required for implementation.
- Public-sector productivity changes and avoided costs from fewer incidents or disputes.
Indirect and opportunity effects
Trace effects through suppliers, downstream users, labour markets and competition. A requirement may increase demand for specialists, alter vendor concentration or shift activity to jurisdictions outside the policy’s reach. Do not count a transfer as a net social benefit simply because money moves from one group to another; report the distribution separately.
How should I quantify costs, benefits and risks?
Quantify significant effects when evidence permits, but keep material qualitative effects visible. Every number needs a scope, source, time horizon, unit, affected population and uncertainty statement.
Use activity-based compliance costing
List each activity induced by the proposal, estimate the time or resources required, identify who performs it, and apply an appropriate cost per unit. Separate initial implementation from recurring work where the evidence supports that distinction. The European Commission’s 21 April 2021 impact assessment used this type of approach for proposed AI-regulation requirements: its supporting EU study estimated administrative and substantive compliance costs from time expenditures on activities triggered by the requirements. Those estimates should not be reused for another policy without checking the underlying scope, assumptions and prices.
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State the accounting frame
Specify whether amounts are financial costs to an actor or wider social costs, and avoid mixing the two. Declare the currency, price year, treatment of taxes and transfers, and the period over which effects are counted. If benefits occur later than costs, explain how future values are compared with present values; do not hide that choice in an unexplained total.
Test assumptions with scenarios
Identify assumptions that could change the decision, such as the number of covered systems, hours per assessment, adoption rates, incident frequency or enforcement intensity. Run low, central and high cases, or a sensitivity analysis that varies one or more assumptions. Report ranges and the drivers of variation rather than a spurious point estimate.
Do not monetise what cannot be credibly priced
Fairness, dignity, rights, trust, contestability and some safety effects may resist credible monetary valuation. Describe their direction, affected groups, evidence and likely scale qualitatively, and explain how decision-makers will weigh them. An unmonetised impact is not a zero impact.
How do I analyse distributional effects?
Report who experiences each cost or benefit, when and where, rather than relying only on an aggregate net figure. Distribution can determine whether an apparently efficient option is acceptable or legally and politically viable.
Create an impact-distribution matrix
| Group | Potential costs | Potential benefits | Evidence and uncertainty |
|---|---|---|---|
| Small and large firms | Compliance labour, capital, delays and lost opportunities | Reduced incident exposure, trust and clearer market rules | Record firm-size coverage and capability assumptions |
| Workers and consumers | Price, access, job or service changes | Safety, accuracy, redress and information | Disaggregate by vulnerability, sector and region where possible |
| Government | Administration, supervision, enforcement and infrastructure | Avoided harms, better services and policy learning | State institutional capacity and funding assumptions |
Consider effects over time: an up-front burden may produce later benefits, while a rule that is affordable for a large organization may be prohibitive for a small one. Explain mitigation options such as phased implementation, guidance or support, but include their costs and possible effects on effectiveness.
How can I evaluate whether the AI policy works?
Build evaluation into the proposal before implementation. AI interventions can change as systems, users and enforcement respond, so the evaluation should cover early rollout, intermediate outputs and longer-term outcomes.
Use a theory of change
For each requirement, specify inputs, activities, immediate outputs, behavioural changes and intended outcomes. Add assumptions and possible unintended consequences, such as paperwork that displaces substantive testing, migration to less-regulated services or risk shifted to people outside the formal scope.
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Choose indicators at several levels
- Implementation: guidance issued, staff trained, systems registered or assessments completed.
- Compliance quality: completeness of documentation, test coverage, remediation time and substantiated complaints.
- Outcomes: incident rates, error or harm measures, access, contestability, productivity and affected-person experience.
- Distribution: results by sector, firm size, demographic group, region and use case.
Define a comparison strategy
Specify the comparison group or counterfactual before collecting results. Randomised experiments may be feasible for some guidance or rollout choices, while quasi-experimental designs or theory-based evaluation may be better for broad legal requirements and complex systems. Explain why the chosen method can separate policy effects from concurrent technology or market changes.
Set review triggers
Assign responsibility, data sources, reporting frequency and decision points for amendment. Include thresholds or qualitative signals that would prompt a review, and preserve flexibility to revise requirements as evidence and AI practices evolve.
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Count the public and private resources required to make the policy operate, not just the cost of writing the rule. An obligation with no realistic supervisory capacity may deliver little benefit while still imposing compliance work.
- Institutional setup: identify the authority, powers, staffing, technical expertise, budget and coordination arrangements.
- Operational processes: cost registration, approvals, audits, inspections, incident reporting, complaints and appeals.
- Enforcement choices: model likely inspection frequency, investigation time, sanctions and judicial review, including escalation for repeated failures.
- Support and guidance: include standards, templates, training, help desks and outreach needed for consistent compliance.
- Data and infrastructure: budget secure systems, interoperability, record retention and analytic capability for monitoring.
Describe how enforcement varies by risk and organizational capacity. If the proposal depends on accredited assessors or external auditors, include their availability, fees and potential bottlenecks as assumptions to test.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should the final assessment be presented for scrutiny?
Present the preferred option and the reasons it performs better against the stated objective and alternatives, while making the analysis auditable. Publish the problem definition, baseline, options, evidence, assumptions, methods, consultation findings, uncertainty and significant impacts.
Use a transparent decision record
- State the decision criteria and any weights or thresholds.
- Show results by actor and distributional group, not only a net total.
- Provide sensitivity or scenario results for assumptions that drive the conclusion.
- Identify impacts that remain qualitative and explain how they were considered.
- Record consultation responses and explain accepted or rejected changes.
- Describe implementation responsibilities, indicators, data protection and review dates.
The European Commission provides one institutional example: significant initiatives receive impact assessments and an independent Regulatory Scrutiny Board checks report quality. Other jurisdictions may use different gates or review bodies; follow the applicable local requirements rather than treating that process as universal.
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The Commission published an impact assessment for a proposed AI regulation on 21 April 2021. A supporting EU study examined administrative and substantive compliance costs by estimating the time needed for activities created by the proposed requirements.
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The useful lesson is methodological: decompose obligations into observable activities, estimate resources for each, and document the population and assumptions. The assessment is not a current or universal estimate for every AI policy. Applying its figures to another jurisdiction, later technology or different policy design without rechecking the underlying scope would be misleading.
What checklist should I use before approving the analysis?
Use this final quality check to find omissions before the proposal is submitted.
- Is the public problem evidenced, and is the objective measurable?
- Are jurisdiction, covered AI activities, actors, exemptions and time horizon explicit?
- Does the baseline include existing rules and expected market and technology change?
- Are no-action, regulatory, non-regulatory and combined alternatives compared?
- Are direct, indirect, opportunity, capital, administrative, implementation and enforcement costs included?
- Are benefits and avoided harms estimated against the same baseline?
- Are businesses, citizens and government analysed separately?
- Are distributional effects shown by relevant groups, place and time?
- Are significant effects quantified with documented sources and assumptions?
- Are qualitative effects such as fairness and rights described rather than assigned zero value?
- Are ranges, sensitivity tests and evidence limitations visible?
- Does the theory of change include assumptions and unintended consequences?
- Are indicators, comparison methods, responsibilities and review triggers specified?
- Can an outside reader reproduce the calculations and understand why the preferred option was chosen?
Frequently Asked Questions
How early should an AI policy impact assessment begin?
Begin while the policy is still being designed, before the text and implementation architecture are fixed. Early analysis can reveal that the objective needs a different instrument, that a proposed duty is infeasible, or that data needed for evaluation will not be collected unless it is planned from the start.
What if reliable data on AI harms or compliance time does not exist?
Use transparent proxies, structured expert elicitation or small pilot measurements, and label each assumption. Present a range and explain how additional data would change the decision; lack of data is a reason to design learning and review, not to assume no impact.
Should an assessment include effects outside the regulating jurisdiction?
Include cross-border effects when firms, supply chains, users or risks can move across borders, then state the boundary of the analysis. Distinguish domestic economic effects from wider global consequences so the same benefit is not counted twice.
How can a small public authority assess a technically complex proposal?
Scale the analysis to the proposal’s significance and risk, use staged pilots or targeted expert input, and document where external technical assurance is needed. A proportionate assessment can be rigorous without modelling every possible system, provided material assumptions and uncertainty are disclosed.
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