Parking Enforcement Workers

Protective Service

AI exposure

  • Data source: BLSPublished: 2026-08

    Moderate· 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

    Source dataset

  • Data source: ILOPublished: 2025

    0.20

    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, 86% 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

Show 13 hidden tasks

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
Respond to and make radio dispatch calls regarding parking violations and complaints.

O*NET Task ID 7939

1.0
Enter and retrieve information pertaining to vehicle registration, identification, and status, using hand-held computers.

O*NET Task ID 7951

1.0
Prepare and maintain required records, including logs of parking enforcement activities, and records of contested citations.

O*NET Task ID 7953

1.0
Assign and review the work of subordinates.

O*NET Task ID 7956

1.0
Write warnings and citations for illegally parked vehicles.

O*NET Task ID 7937

0.5
Identify vehicles in violation of parking codes, checking with dispatchers when necessary to confirm identities or to determine whether vehicles need to be booted or towed.

O*NET Task ID 7941

0.5
Observe and report hazardous conditions, such as missing traffic signals or signs, and street markings that need to be repainted.

O*NET Task ID 7943

0.5
Investigate and answer complaints regarding contested parking citations, determining their validity and routing them appropriately.

O*NET Task ID 7944

0.5
Provide information to the public regarding parking regulations and facilities, and the location of streets, buildings and points of interest.

O*NET Task ID 7946

0.5
Locate lost, stolen, and counterfeit parking permits, and take necessary enforcement action.

O*NET Task ID 7954

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