Transportation Security Screeners
Protective ServiceAI exposure
- Data source: BLSPublished: 2026-08
Moderate· 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: 2026-03
0.000
0.000Range of values carried here0.745Scale, basis and source
- Data source: ILOPublished: 2025
0.20
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, 86% 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
Show 24 hidden tasks| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Provide directions and respond to passenger inquiries.33-9093 | 0.0049100.0 | 0.002246.7 |
Decide whether baggage that triggers alarms should be searched or should be allowed to pass through. | —0 | 0.002553.3 |
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.
| Task | βE1 + 0.5 × E2 |
|---|---|
Check passengers' tickets to ensure that they are valid, and to determine whether passengers have designations that require special handling, such as providing photo identification.O*NET Task ID 15332 | 0.5 |
Contact leads or supervisors to discuss objects of concern that are not on prohibited object lists.O*NET Task ID 15335 | 0.5 |
Decide whether baggage that triggers alarms should be searched or should be allowed to pass through.O*NET Task ID 15337 | 0.5 |
Inform other screeners when baggage should not be opened because it might contain explosives.O*NET Task ID 15339 | 0.5 |
Inspect carry-on items, using x-ray viewing equipment, to determine whether items contain objects that warrant further investigation.O*NET Task ID 15341 | 0.5 |
Inspect checked baggage for signs of tampering.O*NET Task ID 15342 | 0.5 |
Locate suspicious bags pictured in printouts sent from remote monitoring areas, and set these bags aside for inspection.O*NET Task ID 15343 | 0.5 |
Monitor passenger flow through screening checkpoints to ensure order and efficiency.O*NET Task ID 15344 | 0.5 |
Record information about any baggage that sets off alarms in monitoring equipment.O*NET Task ID 15347 | 0.5 |
Test baggage for any explosive materials, using equipment such as explosive detection machines or chemical swab systems.O*NET Task ID 15350 | 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®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page