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Automation risk assessment

Interpreters and Translators: AI exposure and automation risk

Interpreters and Translators score 84% on the Eloundou et al. LLM exposure measure (high exposure). 15 of 17 rated tasks are exposed. BLS projects +2.0% employment, 2025–35.

Data compiled 2026-10-09 · BLS 2025–35 · O*NET 31.0 · AEI May 2026

LLM exposure (β)

84%

High exposure

BLS projects +2.0% employment for interpreters and translators from 2025 to 2035.

Eloundou et al., Science 2024

Exposure scores

How much of this occupation's task list large language models could speed up

84%

High exposure (β, human-rated)

Share of this occupation's tasks that annotators judged an LLM could do at least 50% faster at the same quality, counting tasks that need extra software at half weight.

Eloundou et al., Science 2024

88%

Same measure, rated by GPT-4

The study's model-rated β for comparison. Directly exposed tasks alone (α) score 80%; all exposed tasks (ζ) score 88%.

Eloundou et al., Science 2024

43%

Observed exposure in real AI usage

How much of the theoretically exposed work actually shows up as automated, work-related Claude usage.

Anthropic observed exposure (Mar 2026)

Exposed and unexposed tasks

Human ratings of 17 O*NET task statements, Eloundou et al., Science 2024

Of 17 rated tasks, 13 are directly exposed (E1), 2 are exposed once complementary software is built (E2), and 2 are not exposed (E0).

Exposed tasks

  • Follow ethical codes that protect the confidentiality of information.

    E1 · Directly exposed

  • Translate messages simultaneously or consecutively into specified languages, orally or by using hand signs, maintaining message content, context, and style as much as possible.

    E1 · Directly exposed

  • Listen to speakers' statements to determine meanings and to prepare translations, using electronic listening systems as necessary.

    E1 · Directly exposed

  • Compile terminology and information to be used in translations, including technical terms such as those for legal or medical material.

    E1 · Directly exposed

  • Refer to reference materials, such as dictionaries, lexicons, encyclopedias, and computerized terminology banks, as needed to ensure translation accuracy.

    E1 · Directly exposed

Tasks not exposed

  • Check translations of technical terms and terminology to ensure that they are accurate and remain consistent throughout translation revisions.

    E0 · Not exposed

  • Check original texts or confer with authors to ensure that translations retain the content, meaning, and feeling of the original material.

    E0 · Not exposed

Job outlook

U.S. projections, BLS Employment Projections 2025–35

+2.0%

Projected employment change, 2025–35

From 73,900 jobs in 2025 to 75,400 in 2035. All occupations: +3.5%.

BLS Employment Projections 2025–35

6,000

Openings per year, 2025–35 average

Includes openings from growth and from workers who retire or change occupations.

BLS Employment Projections 2025–35

$60,170

Median annual wage, 2025

BLS Employment Projections 2025–35

Bachelor's degree

Typical education for entry

BLS Employment Projections 2025–35

Primary profiles: BLS Occupational Outlook Handbook · O*NET OnLine

How AI is used on this work

Claude.ai conversations on this occupation's tasks, Anthropic Economic Index (May 2026)

Augmentation (AI works with the person)36.9%
Automation (AI does the task)63.1%

Augmentation and automation are shares of conversations with a classified collaboration pattern, excluding unclassified conversations. The individual pattern shares include them. 38.5% of all conversations on these tasks were for work. See the full collaboration breakdown

Sources & methodology

Every figure on this page comes from the public datasets below. We do not edit the source values; derived figures are explained here.

  1. Exposure is the human-annotator β score from Eloundou et al.: the share of an occupation's O*NET tasks that a large language model could do at least 50% faster at equal quality, counting tasks that need extra software (E2) at half weight. We label β under 0.25 low, 0.25–0.5 moderate, and 0.5 or more high; these bands are ours, not the authors'.
  2. Task labels (E0 not exposed, E1 directly exposed, E2 exposed with complementary software) are the study's aggregated human ratings of each O*NET task statement for the occupation. We show up to five tasks per group.
  3. Observed exposure (Anthropic, March 2026) combines that theoretical feasibility with real Claude usage on the occupation's tasks, weighting automated and work-related use more heavily than augmentative use, then averaging by time spent on each task. Values run from 0 to 1.
  4. Employment, growth, openings, wage, and education are the BLS 2025–35 projections as published; BLS utilization notes are quoted verbatim.
  5. Collaboration shares come from Anthropic Economic Index data for Claude.ai conversations in May 2026, classified to the O*NET tasks they match. "Automation" groups directive and feedback-loop conversations; "augmentation" groups task iteration, learning, and validation. Both buckets are shares of conversations with a classified collaboration pattern, excluding the unclassified "none" pattern; the six individual pattern shares include unclassified conversations. They describe how people use Claude on this occupation's tasks — not what share of workers in the occupation use AI.
  6. BLS occupations are matched to O*NET-SOC codes with the BLS O*NET-to-NEM crosswalk; where a BLS occupation spans several O*NET occupations, we average their values.
  7. Exposure measures what AI could help do, not a forecast of job loss.

  • Eloundou et al., Science 2024

    Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2024). "GPTs are GPTs: Labor market impact potential of LLMs." Science, 384(6702), 1306–1308. doi:10.1126/science.adj0998. Occupation- and task-level exposure data from the authors' public repository (openai/GPTs-are-GPTs).

    Vintage: Published June 21, 2024; human annotations of O*NET tasks collected 2023. License: Data repository: MIT License. Retrieved 2026-10-09.

  • Anthropic observed exposure (Mar 2026)

    Massenkoff, M., & McCrory, P. (2026). "Labor market impacts of AI: A new measure and early evidence." Anthropic, March 5, 2026. Occupation-level observed exposure (Anthropic Economic Index, labor_market_impacts/job_exposure.csv).

    Vintage: Published March 5, 2026. License: CC BY (data). Retrieved 2026-10-09.

  • BLS Employment Projections 2025–35

    U.S. Bureau of Labor Statistics, Employment Projections program. Occupational projections, 2025–35, and worker characteristics, 2025 (Table 1.2); fastest growing occupations (Table 1.3); factors affecting occupational utilization (Table 1.12).

    Vintage: 2025–35 projections, released August 27, 2026. License: Public domain (U.S. federal government work). Retrieved 2026-10-09.

  • Anthropic Economic Index (May 2026)

    Massenkoff, M., Lyubich, E., Sacher, S., Hitzig, Z., Zhang, S., Heller, R., & McCrory, P. (2026). "Anthropic Economic Index report: Cadences." Anthropic, June 26, 2026. Claude.ai usage metrics by occupation, global, May 2026.

    Vintage: Release of June 26, 2026; Claude.ai conversations from May 2026. License: CC BY (data). Retrieved 2026-10-09.

  • O*NET 31.0 Database

    This page includes information from the O*NET 31.0 Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license. O*NET® is a trademark of USDOL/ETA. Metaintro has modified all or some of this information. USDOL/ETA has not approved, endorsed, or tested these modifications.

    Vintage: O*NET 31.0, August 2026 release. License: CC BY 4.0. Retrieved 2026-10-09.

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