Cleaners of Vehicles and Equipment

Transportation and Material Moving Occupations
O*NET-SOC code
53-7061.00

Wash or otherwise clean vehicles, machinery, and other equipment. Use such materials as water, cleaning agents, brushes, cloths, and hoses.

AI exposure

  • Data source: BLSPublished: 2026-08

    Low· 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

    0.000

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

    Observed exposure index, 0–1 as published

    Mapped onto O*NET tasks

    Published per SOC 2018 occupation; every O*NET occupation with the same SOC 2018 code carries this value

    Source dataset (CSV download)

  • Data source: ILOPublished: 2025

    0.09–0.12· 2 ISCO-08 groups

    Scale, basis and source

    Generative AI exposure index, 0–1 as published

    Published per ISCO-08 unit group. Linked to this occupation, wholly or in part, by applying U.S. Bureau of Labor Statistics crosswalks (ISCO-08 to 2010 SOC, 2010 SOC to 2018 SOC) as published

    Source dataset (PDF download)

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.

ILO value for each linked ISCO-08 group
  • ISCO-08 9112Cleaners and Helpers in Offices, Hotels and Other Establishments0.12
  • ISCO-08 9122Vehicle Cleaners0.09

AI exposure (OpenAI rubric)

21 rated tasks · 2 tasks with β ≥ 0.5 (9.5%)

β = direct exposure (E1) + 0.5 × exposure when tools are available (E2), per the source repository's definition.

  • Source unit: O*NET 27.2 tasks → O*NET 31.0 occupation code
  • 1 rated task is not in the O*NET 31.0 task list; its score is kept as published for O*NET 27.2.
Source
OpenAI "GPTs are GPTs" exposure rubric
Release
gh-main-0471612
License
MIT License, Copyright (c) 2024 OpenAI

Tasks

Task statements from the O*NET® 31.0 Database, core tasks first.

TaskTypeβ (OpenAI)
Inspect parts, equipment, or vehicles for cleanliness, damage, and compliance with standards or regulations.Core0.5
Scrub, scrape, or spray machine parts, equipment, or vehicles, using scrapers, brushes, clothes, cleaners, disinfectants, insecticides, acid, abrasives, vacuums, or hoses.Core0
Mix cleaning solutions, abrasive compositions, or other compounds, according to formulas.Core0
Press buttons to activate cleaning equipment or machines.Core0
Clean and polish vehicle windows.Core0
Rinse objects and place them on drying racks or use cloth, squeegees, or air compressors to dry surfaces.Core0
Drive vehicles to or from workshops or customers' workplaces or homes.Core0
Turn valves or handles on equipment to regulate pressure or flow of water, air, steam, or abrasives from sprayer nozzles.Core0
Pre-soak or rinse machine parts, equipment, or vehicles by immersing objects in cleaning solutions or water, manually or using hoists.Core0
Monitor operation of cleaning machines and stop machines or notify supervisors when malfunctions occur.Core0
Connect hoses or lines to pumps or other equipment.Core0
Maintain inventories of supplies.Core1
Apply paints, dyes, polishes, reconditioners, waxes, or masking materials to vehicles to preserve, protect, or restore color or condition.Core0
Turn valves or disconnect hoses to eliminate water, cleaning solutions, or vapors from machinery or tanks.Core0
Sweep, shovel, or vacuum loose debris or salvageable scrap into containers and remove containers from work areas.Core0
Lubricate machinery, vehicles, or equipment or perform minor repairs or adjustments, using hand tools.Supplemental0
Disassemble and reassemble machines or equipment or remove and reattach vehicle parts or trim, using hand tools.Supplemental0
Transport materials, equipment, or supplies to or from work areas, using carts or hoists.Supplemental0
Clean the plastic work inside cars, using paintbrushes.Supplemental0
Fit boot spoilers, side skirts, or mud flaps to cars.Supplemental0

Occupation information

Sources and attribution

This page includes information from the O*NET® 31.0 Database (https://www.onetcenter.org/database.html) by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/). O*NET® is a trademark of USDOL/ETA. AI Changing Work has modified all or some of this information: the O*NET-SOC code, title and task statements are reproduced in English without change; task-type labels are shown in the page's language and tasks are listed core first; any Korean occupation title shown on the Korean-language page is AI Changing Work's translation; any KSCO-8 unit groups linked to this occupation were paired with it by AI Changing Work's judgment, and the relation labels and statuses are AI Changing Work's additions. USDOL/ETA has not approved, endorsed, or tested these modifications.

Any AI exposure figures on this page are published by third parties, not by AI Changing Work, and none is part of the O*NET information. OpenAI publishes task-level scores (MIT License) for O*NET 27.2 task statements; each is shown next to the O*NET 31.0 task statement with the same task ID, whose wording can differ from the 27.2 statement that was scored. OpenAI also publishes occupation-level scores for O*NET-SOC codes in the same release, and any such score is shown on the O*NET occupation with the same code. Anthropic publishes an observed exposure index in the Anthropic Economic Index (CC-BY), and the U.S. Bureau of Labor Statistics publishes relative AI exposure categories (public domain); both are published per SOC code, and each value is shown on every O*NET occupation with that code. The International Labour Organization publishes a generative AI exposure index in ILO Working Paper 140 (CC BY 4.0) for ISCO-08 unit groups; AI Changing Work links those groups to O*NET occupations by applying the U.S. Bureau of Labor Statistics ISCO-08 to 2010 SOC and 2010 SOC to 2018 SOC crosswalks as published, without case-by-case selection, and these crosswalks match many groups only in part. Where several unit groups are linked, each group's published value is listed, and any summary shows only the lowest and highest of those values with the number of groups; no exposure figure is averaged or recalculated. Any employment figures are published by the U.S. Bureau of Labor Statistics for the SOC group containing this occupation. Each source is credited where its figures are shown.

O*NET OnLine: 53-7061.00 Cleaners of Vehicles and Equipment

KSCO 코드·명칭: 한국표준직업분류(제8차 개정) — 통계청 고시 제2024-328호 (2024-07-01 고시, 2025-01-01 시행). 저작권법 제7조 제2호의 고시 항목이다. 명칭 표기(가운뎃점·띄어쓰기)는 해설서 2차 정오 반영판의 표기를 따랐으며, 고시 항목표와는 18개 명칭에서 가운뎃점 글리프나 띄어쓰기만 다르다. 통계청은 2025년 10월 국가데이터처로 개편되었다. 이 페이지의 KSCO 연결은 통계청·국가데이터처의 공식 연계표가 아니다.

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