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An AI impact assessment examines how an AI system will be used, who could be affected, what harms may result, and what safeguards, oversight, and remedies are needed. There is no single assessment method that applies everywhere: legal duties depend on jurisdiction, system, and deployer, while voluntary frameworks can help organisations manage risks even when no specific law requires an assessment.
What an AI impact assessment examines
An assessment looks at an AI system in its real operating context—not just at the technology in isolation. It connects the system’s intended purpose and role in an organisational process to the people who may experience its outputs and the consequences those outputs may have.
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Depending on the applicable law or framework, an assessment may examine effects on fundamental rights, privacy, safety, access to services, or other interests. It should identify plausible harms and specify safeguards, human oversight, responsibility for follow-up, and routes for complaints or redress where appropriate. Completing one does not, by itself, establish that a system is safe, fair, or legally compliant.
When to use one
Use an assessment before deployment if a law or binding policy applicable to your organisation requires it. Separately, consider an assessment as a governance practice whenever an AI system can materially affect people—for example, when its outputs shape access to an opportunity, service, or decision—even if no specific assessment duty has been identified.
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Start by checking the rules for your jurisdiction and your role in the system’s use. A requirement may turn on the type of system, its intended purpose, the decision it supports, and whether you are the provider or deployer. Do not assume that every organisation using AI has the same legal obligation, or that a voluntary framework substitutes for a statutory assessment.
How the main approaches differ
| Approach | Who or what it covers | Status and purpose |
|---|---|---|
| EU AI Act fundamental-rights impact assessment (FRIA) | Specified deployers using certain high-risk AI systems in covered cases | A legal duty under Article 27 of Regulation (EU) 2024/1689 when its conditions apply |
| NIST AI Risk Management Framework (AI RMF) | Organisations managing AI risks to individuals, organisations, society, or the environment | Voluntary risk-management framework; not itself a legal trigger equivalent to Article 27 |
| Government of Canada Algorithmic Impact Assessment (AIA) | Government officials assessing automated decision systems under the applicable federal directive | Government assessment tool used to identify an impact level; not a universal private-sector legal requirement |
These approaches have different jurisdictions, covered roles, purposes, impact methods, controls, reporting rules, and update expectations. A score or result from one should not be treated as interchangeable with another. The OECD’s descriptions of the Canadian tool and public-sector assessment practice provide context for that distinction.
When the EU AI Act requires a FRIA
Article 27 does not impose the same assessment duty on every deployer of every high-risk AI system. It applies before first use in specified cases: to bodies governed by public law and private entities providing public services that deploy covered high-risk systems, and to deployers of certain systems listed in Annex III, points 5(b) and 5(c). The European Commission identifies creditworthiness assessment and life or health insurance pricing and risk assessment among those listed cases. The exact trigger depends on the system’s legal classification, the deployer’s role, the use context, and applicable exceptions.
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For a covered deployment, Article 27 calls for a documented assessment of the impact the system’s use may have on fundamental rights. The information to record includes:
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- the processes in which the system will be used, in line with its intended purpose;
- the intended period and frequency of use;
- the categories of people and groups likely to be affected;
- specific risks of harm to those people or groups, taking provider information into account;
- how human oversight will be implemented; and
- measures to take if risks materialise, including internal governance and complaint mechanisms.
The Act also sets out notification of assessment results to the market surveillance authority using the relevant template, subject to the stated exception. Because classification, exceptions, implementation guidance, and legal interpretations can matter, check the current consolidated text and guidance for the specific deployment before concluding that Article 27 applies.
How to carry out a useful assessment
- Define the decision and context. Record the system, intended purpose, organisational process, expected period and frequency of use, and the decisions or experiences people may encounter as a result.
- Identify affected people and groups. Include both direct users and people subject to system outputs. Consider whether different groups could face different or unequal consequences.
- Map plausible harms. Use information from the system provider, then assess how risks could arise in the particular application. Focus on consequences in context rather than relying only on general statements about the technology.
- Specify safeguards and oversight. Describe what human review will look like, who is responsible for acting on identified risks, and what steps the organisation will take if a risk materialises. Address complaint handling and redress where applicable.
- Coordinate related assessments. Reuse or cross-reference existing documentation only where it genuinely covers the relevant obligations. Make any remaining impacts and context explicit.
- Assign ownership and review dates or triggers. Make clear who maintains the assessment and what changes require it to be reconsidered.
How an AI impact assessment relates to a DPIA
A data protection impact assessment (DPIA) examines data-protection risks. It may contribute to an AI impact assessment, but privacy analysis and analysis of other fundamental-rights impacts are not interchangeable. Article 27 permits relevant sections of a GDPR or law-enforcement DPIA to be cross-referenced or included where they already meet the corresponding FRIA obligations. Any impacts not covered by that material still need to be addressed.
When to revisit the assessment
Review the assessment when relevant information changes or becomes outdated. Practical triggers include a change in the system, intended purpose, affected population, usage frequency, or the process in which it operates. Under Article 27, a covered deployer must update the assessment information when relevant elements change or are no longer current. In similar cases, an earlier assessment may be relied on where appropriate, but it must remain up to date.
What an assessment can—and cannot—tell you
An assessment is a structured way to anticipate and manage impacts; it is not proof that harm will not occur. The frameworks and legal requirements described here do not establish a universal score that makes an AI system safe, nor do they show that one assessment method reduces harm more effectively than another. Use the assessment to make risks, safeguards, accountability, and next steps explicit, then maintain that record as the system and its use evolve.
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