Translators & Interpreters
Arts, Design, Entertainment & MediaAI exposure
- Data source: BLSPublished: '26.08
Very high· relative
LowFour relative bandsVery highGroup-level value
Scale, basis and source
Four relative bands (Low / Moderate / High / Very high)
831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band
- Data source: AnthropicPublished: '26.03
0.430
0.000Range of values carried here0.745Scale, basis and source
- Data source: ILOPublished: '25
0.59
0.09Range of values carried here0.70Group-level value
Scale, basis and source
Generative AI exposure index, 0–1 as published
ISCO-08 unit group — every occupation sharing the code gets this value
Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 5% score at or above this value.
What kind of figure this source publishes
The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.
Task-level exposure
Exposed tasks onlyValues in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.
| Task | βE1 + 0.5 × E2 |
|---|---|
Identify and resolve conflicts related to the meanings of words, concepts, practices, or behaviors.O*NET Task ID 9327 | 1.0 |
Translate messages simultaneously or consecutively into specified languages, orally or by using hand signs, maintaining message content, context, and style as much as possible.O*NET Task ID 9328 | 1.0 |
Proofread, edit, and revise translated materials.O*NET Task ID 9329 | 1.0 |
Check translations of technical terms and terminology to ensure that they are accurate and remain consistent throughout translation revisions.O*NET Task ID 9330 | 1.0 |
Read written materials, such as legal documents, scientific works, or news reports, and rewrite material into specified languages.O*NET Task ID 9331 | 1.0 |
Refer to reference materials, such as dictionaries, lexicons, encyclopedias, and computerized terminology banks, as needed to ensure translation accuracy.O*NET Task ID 9332 | 1.0 |
Compile terminology and information to be used in translations, including technical terms such as those for legal or medical material.O*NET Task ID 9333 | 1.0 |
Adapt translations to students' cognitive and grade levels, collaborating with educational team members as necessary.O*NET Task ID 9334 | 1.0 |
Listen to speakers' statements to determine meanings and to prepare translations, using electronic listening systems as necessary.O*NET Task ID 9335 | 1.0 |
Check original texts or confer with authors to ensure that translations retain the content, meaning, and feeling of the original material.O*NET Task ID 9336 | 1.0 |
Follow ethical codes that protect the confidentiality of information.O*NET Task ID 9326 | 0.0 |
Travel with or guide tourists who speak another language.O*NET Task ID 9342 | 0.0 |
β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.
Data sources & licenses — O*NET®, Eloundou et al. (2023): see full notices on the Credits page
Occupation information
Recent Changes Affecting This Occupation
Mar 2026: New evergreen blog post: translators face highest disruption in arts/media. 68% automation risk, 74% exposure.
[Source: AI Changing Work Blog]