Cartographers

Life, Physical & Social Sciences

AI exposure

  • Data source: BLSPublished: 2026-08

    High· relative

    LowFour relative bandsVery high

    Group-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

    Source dataset (XLSX download)

  • Data source: AnthropicPublished: 2026-03

    0.080

    0.000Range of values carried here0.745
    Scale, basis and source

    Observed exposure index, 0–1 as published

    Mapped onto O*NET tasks

    Source dataset

  • Data source: ILOPublished: 2025

    0.44

    0.09Range of values carried here0.70

    Group-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, 28% score at or above this value.

    Source dataset

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

Values 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.

TaskβE1 + 0.5 × E2
Determine guidelines that specify which source material is acceptable for use.

O*NET Task ID 3525

1.0
Identify, scale, and orient geodetic points, elevations, and other planimetric or topographic features, applying standard mathematical formulas.

O*NET Task ID 3514

0.5
Collect information about specific features of the Earth, using aerial photography and other digital remote sensing techniques.

O*NET Task ID 3515

0.5
Revise existing maps and charts, making all necessary corrections and adjustments.

O*NET Task ID 3516

0.5
Compile data required for map preparation, including aerial photographs, survey notes, records, reports, and original maps.

O*NET Task ID 3517

0.5
Inspect final compositions to ensure completeness and accuracy.

O*NET Task ID 3518

0.5
Determine map content and layout, as well as production specifications such as scale, size, projection, and colors, and direct production to ensure that specifications are followed.

O*NET Task ID 3519

0.5
Examine and analyze data from ground surveys, reports, aerial photographs, and satellite images to prepare topographic maps, aerial-photograph mosaics, and related charts.

O*NET Task ID 3520

0.5
Select aerial photographic and remote sensing techniques and plotting equipment needed to meet required standards of accuracy.

O*NET Task ID 3521

0.5
Delineate aerial photographic detail, such as control points, hydrography, topography, and cultural features, using precision stereoplotting apparatus or drafting instruments.

O*NET Task ID 3522

0.5

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