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AI translation can produce a quick draft, but speed alone does not show whether the result is safe to use. Choose among unedited machine translation, machine translation with post-editing, and human translation by weighing the likelihood of errors against their consequences—and by deciding what evidence and review are needed before release.
Start with the consequence of being wrong
A translation for an employee who needs the gist of a document has different stakes from a public instruction, contract, health notice, or text that could affect someone’s rights or money. The European Commission’s Directorate-General for Translation describes the governing principle as a risk-based approach: consider both how likely an error is and what it could cause.
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That is an operational quality decision, not a legal classification. A translation task is not automatically subject to a particular AI-law obligation simply because machine translation is used. Assess the content and use first; determine legal duties separately where applicable.
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- Purpose and audience: Is this for internal understanding, a temporary working draft, publication, or a text that will have legal or practical effect?
- Plausible errors: Could a mistranslated number, negation, warning, name, or technical term change what someone does or understands?
- Likely impact: Could the mistake create confusion, financial loss, harm, or an effect on health, safety, or rights?
- Evidence needed: What evaluation and sampling would give you enough confidence to release this content?
- Review authority: Does the reviewer have the language-pair expertise and subject knowledge to identify consequential mistakes and require corrections?
These questions do not create a universal risk score or automatic category. They help an organization decide what its own content and audience require.
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Choose the workflow that fits the stakes
Three broad options are useful to distinguish. Their relative speed, cost, and accuracy depend on the language pair, subject matter, terminology, review capacity, and the organization’s own results; no option is best in every case.
| Workflow | Practical fit | Trade-off to assess |
|---|---|---|
| Unedited machine translation | Quick gist or low-consequence internal content when the organization accepts the possibility of errors | Fast, low-friction output may contain inconsistent terminology or meaning errors |
| Machine translation with post-editing | Content that needs stronger quality control and can be checked and corrected by a competent post-editor | Machine output can provide a draft, but review takes time and requires relevant language expertise |
| Human translation | Content that needs human composition and contextual judgment | Uses human expertise, with turnaround and cost to assess for the specific task |
These are workflow choices, not guaranteed performance rankings. Compare them using evidence from your own content and language pairs, including turnaround, reviewer capacity, terminology handling, and error patterns.
When an internal draft may be enough
Unedited output may be reasonable for a low-consequence internal gist if readers understand that it is not a verified translation and will not rely on it as an authoritative version. If the draft begins influencing a decision, is shared externally, or becomes the basis for instructions or commitments, reassess the workflow before use.
When post-editing is a better fit
Use post-editing when a machine-generated draft is useful but the text needs a human to check meaning, terminology, and context. Match the reviewer to the content: general language competence may not be enough for technical, legal, health, or safety material. Agree in advance on what counts as an error and who can hold the text from release.
When human translation is warranted
Human translation is appropriate to consider when the content requires substantial contextual judgment or when a meaning error could have material consequences. Human work still needs an evaluation process suited to the intended use; choosing a human translator does not by itself establish that an output is correct.
Set an evaluation method before release
ISO 5060:2024, titled Translation services — Evaluation of translation output — General guidance, provides guidance for evaluating human translation, post-edited machine translation, and unedited machine translation. It uses error types and penalty points to produce an error score and quality rating, and addresses evaluator competence and sampling. Its stated scope is human evaluation; it does not apply to interpreting and does not cover the broader processes of quality assurance and corrective action.
The standard offers a way to structure evaluation, not a universal quality threshold or a promise that a particular workflow will pass. For a release decision, define the evaluation approach and sampling plan in advance. Choose qualified evaluators, decide which error types matter for the content, and establish what findings require correction or escalation.
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Make evaluation reflect actual use
- Evaluate the language pair and subject matter you intend to publish, rather than assuming results transfer across languages or content types.
- Include checks for errors with meaningful consequences, such as changed instructions, omitted qualifications, or inconsistent key terms.
- Record how sampled material was selected and what the evaluator was qualified to assess.
- Use evaluation findings to decide whether to accept, correct, or reject the output under your own release criteria.
Separate editorial oversight from legal obligations
Editorial review is a prudent way to manage translation risk, but it should not be confused with a claim that every translation system is legally classified as high-risk. Under Article 14 of the EU AI Act, effective human oversight is required for AI systems that meet the Act’s high-risk classification. Oversight measures are to be proportionate to risks, autonomy, and context. The article addresses measures that enable overseers to understand system limitations, recognize automation bias, interpret outputs, override them, or stop system operation.
Whether a particular system falls within that classification depends on the Act and the system’s context; translation as a broad activity is not automatically high-risk under Article 14. The European Commission’s classification guidance page describes its high-risk classification guidelines as draft, non-binding guidance and notes an updated application timeline following political agreement on the AI Omnibus. Check the current law and Commission guidance when making a compliance decision.
The NIST AI Risk Management Framework is a voluntary resource for incorporating trustworthiness considerations into AI design, development, use, and evaluation. It is not a translation-specific standard or a legal mandate. NIST says the framework is being revised; its Generative AI Profile was released on July 26, 2024. Check NIST’s current materials before relying on a version for governance planning.
Keep the decision current
Translation risk can change when content, audience, purpose, terminology, or workflow changes. Treat the initial choice as a decision to revisit, not a permanent approval of a tool or process.
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- Update terminology and review guidance when an organization’s preferred terms change.
- Reassess after a model, process, reviewer, or intended use changes.
- Escalate content when it shifts from internal understanding to publication or practical reliance.
These are practical governance recommendations, not additional requirements stated by ISO 5060 or NIST. Their purpose is to ensure the chosen level of review continues to fit the consequences of an error.
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