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What an AI impact assessment does
An AI impact assessment is a structured examination of an AI system in its real operating context. It considers the process in which the system will be used, the people who may experience its outputs, possible harms, and the measures for preventing or responding to those harms.
The term covers several approaches rather than one standard form or score. Some assessments are required by law for specified systems and deployers; others are voluntary risk-management practices or tools designed for a particular public administration. Completing an assessment does not, by itself, establish that a system is safe, fair, or legally compliant.
When should you use one?
First check whether the law that applies to your organization and use case requires an assessment. If it does, follow that law’s scope, content, timing, and reporting rules. Separately, consider an assessment as a governance measure when a system may meaningfully influence people’s access to services, opportunities, treatment, or rights—even if no specific assessment duty applies.
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For an EU deployment, Article 27 of Regulation (EU) 2024/1689 creates a specific Fundamental Rights Impact Assessment (FRIA) duty for certain deployers and high-risk AI systems. It is not a blanket requirement for every organization using AI or every high-risk AI system. The trigger depends on the system’s legal classification, the deployer’s role, and the use context. The European Commission identifies creditworthiness assessment and life or health insurance pricing and risk assessment among the specified cases. Check the applicable provision, exceptions, and current guidance before deciding whether Article 27 applies.
The EU AI Act Service Desk describes its consolidated text as current to 27 July 2026. Because implementation guidance, templates, amendments, and national interpretations can change, verify the applicable rules for your jurisdiction and deployment before relying on a legal conclusion.
How the main approaches differ
| Approach | Where and for whom | Role | What to know |
|---|---|---|---|
| EU AI Act Article 27 FRIA | Specified deployers of covered high-risk AI systems in the EU | Legal requirement in the cases set out by Article 27 | Must be performed before first use in covered cases; the Act specifies assessment content and update and notification duties, subject to stated exceptions. |
| NIST AI Risk Management Framework (AI RMF) | Organizations managing AI risks affecting individuals, organizations, society, or the environment | Voluntary risk-management framework | Supports consideration of trustworthiness through AI design, development, use, and evaluation. It is not itself a legal trigger equivalent to Article 27. |
| Government of Canada Algorithmic Impact Assessment (AIA) | Canadian federal government officials assessing automated decision systems under the Directive on Automated Decision-Making | Government assessment tool | The OECD describes it as helping officials assess these systems and identify an impact level. It should not be treated as a universal private-sector legal requirement. |
Choose an approach by comparing its jurisdiction and legal status, covered systems and organizational roles, harms or rights in scope, assessment method, required controls, stakeholder involvement, and update or reporting rules. These approaches serve different purposes; their outputs should not be collapsed into one universal “AI impact assessment” score.
What an assessment should cover
For an EU FRIA, Article 27 specifies the following information. These elements also provide a useful reference when designing a broader organizational assessment, even where the article does not apply as a legal requirement.
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- Use and process: the organizational process in which the system will be used, in line with its intended purpose.
- Timing and frequency: the intended period of use and how often the system will be used.
- Affected people: the categories of individuals and groups likely to be affected.
- Risks of harm: the specific risks to those people or groups, taking information from the system provider into account.
- Human oversight: how oversight will work in practice.
- Response measures: what the organization will do if risks materialize, including relevant internal governance and complaint mechanisms.
The assessment should focus on the system’s application in context, not only general claims about the technology. Consider how different groups may experience the process and its outcomes. Where appropriate, the EU Act’s Recital 96 describes involving representatives of affected groups, independent experts, and civil society, as well as considering complaint handling and redress.
A practical way to conduct the assessment
- Define the use case. Record which AI system is involved, its intended purpose, the organizational process it supports, when and how often it will be used, and the decisions or experiences people may face as a result.
- Identify affected people and groups. Include people who are directly subject to outputs as well as users of the system. Consider whether groups could face different consequences in this particular context.
- Map plausible harms. Combine relevant provider information with an analysis of the actual application. Identify who could be harmed, how the harm might occur, and what features of the process could contribute to it.
- Specify safeguards and accountability. Document how human oversight will operate, who owns each governance action, what happens if a risk occurs, and how affected people can complain or seek redress where applicable.
- Coordinate related assessments. Reuse or cross-reference existing documentation only where it actually covers the relevant obligations and impacts. Record any remaining analysis separately.
- Set a review plan. Assign responsibility for keeping the assessment current and define the changes that will trigger a review.
Is an AI impact assessment the same as a DPIA?
No. A data protection impact assessment (DPIA) addresses data-protection obligations; an AI impact assessment may examine a wider set of effects on people. Under Article 27, relevant sections of a GDPR or law-enforcement DPIA may be cross-referenced or included if they already meet the corresponding FRIA requirements. This allows coordinated documentation, but a DPIA does not automatically replace a FRIA or make privacy and fundamental-rights analysis interchangeable.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When to revisit an assessment
An assessment is not a one-time guarantee. Revisit it when relevant elements change or become outdated—for example, if the system, its intended purpose, the affected population, or the frequency of use changes. Article 27 applies to first use and requires the information to be updated when relevant elements change or are no longer current. In similar cases, a deployer may rely on earlier assessments where appropriate, but must still ensure the information remains current.
For covered Article 27 cases, the deployer must notify the market surveillance authority of the assessment results using the applicable template, subject to the stated exception. Confirm the current process and any exception that applies to your case.
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What an assessment can—and cannot—establish
An assessment makes assumptions, potential harms, safeguards, responsibilities, and review needs more explicit. It does not prove that harm will not occur, guarantee legal compliance, or supply a universal pass/fail score. NIST’s AI RMF is voluntary, while the Canadian AIA serves a government decision-making context; their distinct roles are reasons to match the method to the system, jurisdiction, and affected people rather than treating all assessments as equivalent.
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