Gaming Surveillance Officers
Protective ServiceAI exposure
- Data source: BLSPublished: 2026-08
High· 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
Exposed tasks only| Task | APIRaw / share % |
|---|---|
Monitor establishment activities to ensure adherence to all state gaming regulations and company policies and procedures.33-9031 | 0.0015100.0 |
| Not observed on any surface — 4 task(s) — These tasks have no row in the source for this release. The 0 in the share row is a display-stage composition ratio; absence is what the — in the raw row states. | |
Observe casino or casino hotel operations for irregular activities such as cheating or theft by employees or patrons, using audio and video equipment and one-way mirrors. | —0 |
Report all violations and suspicious behaviors to supervisors, verbally or in writing. | —0 |
Act as oversight or security agents for management or customers. | —0 |
Supervise or train surveillance observers. | —0 |
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 |
|---|---|
Report all violations and suspicious behaviors to supervisors, verbally or in writing.O*NET Task ID 4408 | 1.0 |
Develop and maintain log of surveillance observations.O*NET Task ID 21145 | 1.0 |
Observe casino or casino hotel operations for irregular activities, such as cheating or theft by employees or patrons, using audio and video equipment and one-way mirrors.O*NET Task ID 4407 | 0.5 |
Inspect and monitor audio or video surveillance equipment to ensure it is working appropriately.O*NET Task ID 21146 | 0.5 |
Review video surveillance footage.O*NET Task ID 21147 | 0.5 |
Monitor establishment activities to ensure adherence to all state gaming regulations and company policies and procedures.O*NET Task ID 4409 | 0.0 |
Act as oversight or security agents for management or customers.O*NET Task ID 4410 | 0.0 |
Supervise or train surveillance observers.O*NET Task ID 4411 | 0.0 |
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