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Assess a proposed AI policy by comparing it with a clearly defined no-action baseline and realistic alternatives. First specify the jurisdiction, affected AI activities, policy objective and causal mechanism. Then map costs, benefits, risks and distributional effects for businesses, people and government; quantify material effects where evidence supports it; describe important effects that cannot be monetized; and publish assumptions, uncertainty and an evaluation plan.
There is no universal economic figure for “an AI policy.” The result depends on the proposal’s design, the market and legal setting, who must comply, the time horizon and the evidence available. The method below gives you a defensible assessment without pretending that uncertain or non-market effects are zero.
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What must you specify before estimating an AI policy’s impact?
A policy cannot be assessed in the abstract. Record the jurisdiction, legal authority, affected sectors and the exact intervention before collecting numbers.
Define the jurisdiction and legal setting
State the country, state or other jurisdiction, the level of government, and the rules already in force. Existing privacy, consumer-protection, safety, labor, procurement or sector rules may change both the incremental cost of a new measure and the people responsible for enforcement. Note whether the proposal would apply to developers, deployers, public purchasers, distributors, users, or several of these groups.
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Describe the policy design precisely
Write the requirements in operational terms: prohibited practices, approval or registration steps, documentation, testing, disclosure, human oversight, incident reporting, audits, penalties, exemptions and effective dates. Distinguish obligations for development, deployment, procurement and a particular use case. A broad “AI safety law” cannot be costed until its covered systems and duties are specified.
Set the objective and success measure
Express the public objective as an outcome, such as fewer harmful decisions, safer critical infrastructure or more trustworthy public services. Define how success will be observed and over what period. An objective that cannot be measured will make later evaluation and comparison largely subjective.
How do you define the problem and the policy’s causal mechanism?
Start with evidence that the problem exists, then explain how the proposed requirement is expected to change behavior and outcomes. This is the foundation of a regulatory impact assessment.
Describe the problem, not just the technology
Identify the harm or missed opportunity, its frequency and severity, and the groups exposed to it. Separate a documented market failure from a concern that is plausible but not yet measured. Explain whether existing rules, contracts, professional standards or market incentives already address part of the problem.
Draw a theory of change
Connect each requirement to a chain of effects:
- Input: the legal duty, such as pre-deployment testing or an incident-reporting obligation.
- Immediate behavior: what a regulated organization must do differently, including staff time, process changes or technical controls.
- Intermediate outcome: improved detection, fewer unsafe releases, better user information or changed purchasing decisions.
- Final outcome: reduced harm, higher productivity, improved trust or another stated objective.
List the assumptions at each link. For example, a disclosure rule may improve decisions only if people can understand the information and have a realistic alternative. Note possible unintended consequences, such as organizations withdrawing a useful low-margin service or concentrating the market in firms able to absorb fixed compliance costs.
What is the right baseline for an AI policy assessment?
The baseline is the most credible description of what would happen without the proposal. Compare every option against it, not against an unrealistic world in which no rules or technology changes.
Build a no-action scenario
Include existing laws, announced regulations, expected technology adoption, demographic or economic trends, and likely private-sector responses. Specify the baseline date and forecast period. If an existing rule is scheduled to change, include that change rather than treating it as absent.
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Use more than one baseline when uncertainty is material
If adoption, incident rates or enforcement capacity could evolve in different ways, create low, central and high scenarios. Keep the policy constant while changing the external assumptions so readers can see which conclusions depend on the forecast.
Document exclusions
State what the baseline does not include. For example, a voluntary industry code should not be counted as a binding requirement unless evidence indicates broad compliance. Exclusions prevent an apparently precise estimate from quietly assuming away important effects.
Which alternatives should you compare with the proposal?
Compare the proposed design with feasible regulatory and non-regulatory approaches. The preferred option should perform best against the stated objective after costs, distribution, uncertainty and implementation capacity are considered.
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|---|---|---|
| No action | What happens under current rules and expected market change? | Lowest new administrative burden, but the identified problem may persist. |
| Information or education | Would guidance, labels or professional training change behavior? | Usually less intrusive, but effectiveness may depend on attention and voluntary uptake. |
| Voluntary standards or codes | Who adopts them, and are commitments verifiable? | Faster adaptability, with weaker coverage and enforcement. |
| Targeted regulation | Can duties focus on high-risk systems, sectors or uses? | May reduce unnecessary burden, but boundary decisions can leave gaps. |
| Broad regulation | Does a common requirement address risks across many uses? | More uniform protection, but potentially higher fixed and recurring costs. |
| Mixed package | Would regulation plus guidance, procurement rules or support work better? | Can address several failure points while increasing coordination costs. |
Use the same baseline, time horizon and outcome measures for every option. Make trade-offs explicit rather than ranking an option solely by its headline benefit or the regulator’s own budget.
What costs, benefits and risks belong in the analysis?
Count impacts across businesses, citizens and government. Separate one-time changes from recurring effects and identify who experiences each item.
Business and organizational costs
- Administrative costs: staff time for documentation, registration, reporting, consultations and record keeping.
- Technical and capital costs: testing infrastructure, monitoring tools, data controls, model changes, cybersecurity and equipment.
- Professional services: legal, audit, assurance, risk and specialist expertise.
- Opportunity costs: delayed launches, foregone projects, reduced experimentation or staff diverted from productive work.
- Recurring compliance: periodic testing, incident response, updates, training, audits and supervision.
Where evidence permits, show one-time implementation costs separately from annual or event-driven costs. A small firm and a large platform may face the same formal duty but very different average costs.
Costs and benefits for people
Include changes in prices, waiting time, access, employment, wages, privacy, safety, quality and consumer choice. A requirement that prevents an automated error may create benefits that do not appear as a market transaction. Conversely, a delay or withdrawal of a service can impose costs even if no invoice records them.
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Government and enforcement effects
Estimate regulator staffing, training, technical capability, inspections, complaint handling, investigations, adjudication, procurement and public communication. Include transition costs and the resources needed to monitor compliance. Enforcement that is legally available but underfunded should not be treated as costless or fully effective.
Benefits and avoided harms
Use the same baseline and time horizon to estimate prevented incidents, reduced losses, improved reliability, productivity gains, trust, access or other outcomes. Explain the evidence linking the requirement to each benefit. Avoid counting a transfer—such as a fee paid by one party to government—as a net social benefit unless it changes real resource use or behavior.
Risks and unintended effects
Consider regulatory arbitrage, migration of activity to another jurisdiction, reduced competition, barriers to entry, substitution toward less transparent tools, and impacts on innovation. Record who bears these risks and whether they are reversible.
How should you quantify costs and benefits?
Use analysis proportionate to the proposal’s significance. Significant impacts should be quantified where credible evidence exists, while important effects that resist monetization should remain visible in qualitative form.
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Choose a time horizon and price basis
State the start date, forecast period and whether values are current-year or constant prices. Explain how you treat recurring costs, replacement cycles and delayed benefits. If discounting is required in your jurisdiction, disclose the rate and its source rather than presenting a single undisclosed present-value total.
Estimate compliance costs from activities
List each induced activity—such as a risk assessment, test, audit or report—and estimate the quantity of work, the type of personnel, the unit cost and the frequency. Show the population of affected organizations and the share expected to undertake each activity. This activity-based approach is more transparent than assigning a percentage of revenue without explaining what the percentage represents.
Use ranges and scenarios
Present a central estimate alongside plausible low and high cases when adoption, incident rates, wage costs or enforcement intensity are uncertain. Identify the assumptions that drive the range. Sensitivity analysis should show how the result changes when one key assumption changes; scenario analysis can change several linked assumptions together.
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Do not monetize what cannot be measured credibly
Fairness, dignity, privacy, democratic participation, distributional effects and some safety improvements may lack reliable prices. Describe their direction, affected groups, evidence strength and decision relevance. “Not monetized” means the value is uncertain or non-market—not zero.
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For every material input, record its source, date, population, geography, measurement method, unit, confidence and any adjustment made for the proposal. Distinguish observed data from expert judgment and forecasts. This allows reviewers to reproduce the calculation and challenge a weak assumption without discarding the entire assessment.
How do you analyze distribution and equity?
An aggregate net benefit can conceal losses for a specific group. Report how impacts are distributed across firm size, sector, income, occupation, geography, age, disability status and other relevant characteristics.
Map winners, losers and timing
For each cost and benefit, identify the group affected, when the effect occurs and whether it is temporary or persistent. A fixed compliance cost may be manageable for a large provider but material for a small entrant. A safety benefit may arrive years after an immediate price increase.
Test design choices that change distribution
Analyze thresholds, exemptions, phased dates, support for small organizations, public procurement conditions and geographic coverage. Explain whether each choice improves proportionality or creates a loophole. Distributional analysis should inform the design, not appear only as a closing paragraph.
What evaluation plan should accompany an AI policy?
Build monitoring and evaluation into the proposal before implementation. AI interventions can change as systems, markets and organizational practices evolve, so the plan should cover early rollout and longer-term outcomes.
Define indicators at each stage
- Implementation: registrations completed, guidance issued, staff trained, audits performed and response times.
- Behavior: testing coverage, incident reporting, remediation rates, procurement changes and user disclosures.
- Outcomes: incident frequency and severity, error disparities, service quality, costs, access and productivity.
- Unintended effects: market exit, migration, substitution, complaints or new disparities.
Choose a comparison strategy
Where feasible, compare affected units with a credible comparison group. Randomized designs may be appropriate for some guidance or support interventions. Quasi-experimental approaches can use phased implementation, eligibility thresholds or before-and-after changes with suitable controls. When those designs are impractical, a theory-based evaluation can test whether each link in the causal chain occurred and why.
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Set review triggers and adaptation rules
Specify who reviews the evidence, how often, and what would trigger amended guidance, enforcement priorities, exemptions or repeal. Preserve data needed for evaluation while respecting privacy and confidentiality. An evaluation that cannot distinguish non-compliance from an ineffective policy will not support useful revision.
What can the European Commission’s 2021 AI proposal teach about compliance costs?
The European Commission published an impact assessment for its proposed AI regulation on 21 April 2021. A supporting European Union study examined administrative and substantive compliance costs by estimating the time required for activities induced by the proposed requirements.
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This is a useful model for structuring a cost estimate: identify the obligation, list the activity it creates, estimate time and personnel, and scale the result to the affected population. It is not a current or universal price list. Its estimates reflect that proposal’s design, assumptions, scope and date; applying them to another jurisdiction or a different AI policy would require checking and rebuilding the underlying inputs.
How should you present the final assessment?
A decision-maker should be able to see what was compared, how the numbers were produced and what remains uncertain.
Use a transparent decision table
| Criterion | Option A | Option B | Option C |
|---|---|---|---|
| Effectiveness against the defined problem | Evidence, expected outcome and confidence | Evidence, expected outcome and confidence | Evidence, expected outcome and confidence |
| Total costs and benefits | One-time, recurring and monetized effects | One-time, recurring and monetized effects | One-time, recurring and monetized effects |
| Distribution | Groups affected and timing | Groups affected and timing | Groups affected and timing |
| Uncertainty | Key assumptions and evidence strength | Key assumptions and evidence strength | Key assumptions and evidence strength |
| Implementation and adaptability | Capacity, enforcement and review plan | Capacity, enforcement and review plan | Capacity, enforcement and review plan |
Publish the assumptions and consultation record
Explain the data, methods, model boundaries, scenario choices and sensitivity results. Summarize consultation responses, identify disagreements that changed the analysis and disclose significant effects that could not be monetized. Publishing the reasoning lets affected people challenge an assumption rather than merely accepting a headline total.
State the recommendation narrowly
Recommend the option that best meets the defined objective under the stated assumptions and capacity constraints. Say what evidence would change that recommendation. Do not claim that a policy is economically beneficial in every setting when the analysis covers only one jurisdiction, sector or design.
What are the most common mistakes?
- No-action is omitted: without a baseline, the analysis cannot show additional effects.
- The proposal is vague: undefined coverage and duties make costs and benefits unknowable.
- Only the regulator’s budget is counted: private compliance, citizen effects and opportunity costs are missed.
- Benefits are asserted, not linked: the causal mechanism and evidence are absent.
- Precision is overstated: a single number hides uncertain adoption, timing or enforcement.
- Qualitative effects disappear: non-market and distributional impacts are treated as zero.
- Implementation is ignored: a rule is assumed to work regardless of staffing, guidance or enforcement.
- Evaluation is an afterthought: no indicators or comparison strategy exists to test whether the policy worked.
- Evidence is transferred without checking scope: an estimate from another proposal, date or jurisdiction is presented as universal.
What should be on your assessment checklist?
- Jurisdiction, legal context, covered systems and regulated actors are specified.
- The problem, objective, theory of change and success measures are documented.
- A dated business-as-usual baseline includes existing rules and expected market change.
- Feasible regulatory, non-regulatory and mixed alternatives are compared.
- Costs, benefits, risks and opportunity costs are mapped for businesses, people and government.
- One-time and recurring effects, time horizon and price basis are stated.
- Material effects are quantified with sources, assumptions, ranges and sensitivity tests.
- Important non-monetized, equity and distributional effects are described.
- Implementation, enforcement, monitoring and adaptation resources are costed.
- Indicators, a comparison strategy, review schedule and revision triggers are set.
- Consultation findings, methods and uncertainty are published for scrutiny.
Frequently Asked Questions
How long should an AI policy impact assessment take?
The appropriate schedule depends on the proposal’s significance, data availability and consultation requirements. Set a minimum timetable that allows baseline construction, option comparison, review of assumptions and an evaluation design; rushing those steps usually creates more rework after adoption.
Can a small organization perform this analysis without an economic model?
Yes. A spreadsheet that lists induced activities, affected units, unit costs, timing and scenarios can be more transparent than a complex model with hidden assumptions. Seek specialist review for legal interpretation, valuation of major risks or statistically designed evaluation.
What if there is no reliable estimate of an AI system’s harm rate?
Use bounded scenarios, analogous evidence and expert elicitation, and label the confidence level. Continue with qualitative analysis of severity, reversibility and who is exposed; lack of a precise probability does not justify assigning the harm a value of zero.
When should an AI policy be revisited after adoption?
Choose review points before implementation, with an early check focused on compliance and unintended effects and a later check focused on outcomes. Also define event-based triggers, such as a material incident pattern, technology change or evidence that a covered activity has moved outside the policy’s scope.
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