検針員
事務・管理サポートAI露出度
- データ出典: BLS公表時点: 2026-08
中程度· 相対
低い4段階の相対区分非常に高い職業群単位の値
尺度・母数・出典
4段階の相対区分（低い / 中程度 / 高い / 非常に高い）
BLS雇用見通し表の詳細職業831件が母数。値は NEM(全国雇用マトリクス)コード単位で付与されるため、同じ NEM コードの職業は同じバンドになります
- データ出典: Anthropic公表時点: 2026-03
0.000
0.000ここに掲載された値の範囲0.745尺度・母数・出典
- データ出典: ILO公表時点: 2025
0.38
0.09ここに掲載された値の範囲0.70職業群単位の値
尺度・母数・出典
生成AI露出度指数、公開されたまま0–1
ISCO-08の職業小分類単位 — 同じコードの職業はすべて同じ値
当サイトの算出であり、ILOが公表した数値ではありません。当サイトがILOデータセットに結び付けた職業1,012件のうち、この値以上のものは48%です。
この出典がどのような性格の値か
BLSの区分は絶対水準ではなく相対順位であり、一次測定でもありません。複数の既存研究が付けた職業別パーセンタイル順位を4段階にまとめた値です。雇用や賃金の予測でもなく、導入確率でもなく、自動化と増強を区別しません。
Task-level exposure
露出のある作業のみ| Task | APIRaw / share % |
|---|---|
Read electric, gas, water, or steam consumption meters and enter data in route books or hand-held computers.43-5041 | 0.0019100.0 |
| Not observed on any surface — 11 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. | |
Walk or drive vehicles along established routes to take readings of meter dials. | —0 |
Upload into office computers all information collected on hand-held computers during meter rounds, or return route books or hand-held computers to business offices so that data can be compiled. | —0 |
Verify readings in cases where consumption appears to be abnormal, and record possible reasons for fluctuations. | —0 |
Inspect meters for unauthorized connections, defects, and damage such as broken seals. | —0 |
Report to service departments any problems such as meter irregularities, damaged equipment, or impediments to meter access, including dogs. | —0 |
Answer customers' questions about services and charges, or direct them to customer service centers. | —0 |
Update client address and meter location information. | —0 |
Leave messages to arrange different times to read meters in cases in which meters are not accessible. | —0 |
Connect and disconnect utility services at specific locations. | —0 |
Collect past-due bills. | —0 |
Report lost or broken keys. | —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 |
|---|---|
Upload into office computers all information collected on hand-held computers during meter rounds, or return route books or hand-held computers to business offices so that data can be compiled.O*NET Task ID 11311 | 1.0 |
Answer customers' questions about services and charges, or direct them to customer service centers.O*NET Task ID 11315 | 1.0 |
Update client address and meter location information.O*NET Task ID 11316 | 1.0 |
Leave messages to arrange different times to read meters in cases in which meters are not accessible.O*NET Task ID 11317 | 1.0 |
Read electric, gas, water, or steam consumption meters and enter data in route books or hand-held computers.O*NET Task ID 11309 | 0.5 |
Verify readings in cases where consumption appears to be abnormal, and record possible reasons for fluctuations.O*NET Task ID 11312 | 0.5 |
Collect past-due bills.O*NET Task ID 11319 | 0.5 |
Walk or drive vehicles along established routes to take readings of meter dials.O*NET Task ID 11310 | 0.0 |
Inspect meters for unauthorized connections, defects, and damage, such as broken seals.O*NET Task ID 11313 | 0.0 |
Report to service departments any problems, such as meter irregularities, damaged equipment, or impediments to meter access, including dogs.O*NET Task ID 11314 | 0.0 |
Connect and disconnect utility services at specific locations.O*NET Task ID 11318 | 0.0 |
Report lost or broken keys.O*NET Task ID 11320 | 0.0 |
Perform preventative maintenance or minor repairs on meters.O*NET Task ID 20904 | 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