インテリジェンス作戦専門家
保護サービスAI露出度
- データ出典: BLS公表時点: 2026-08
高い· 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
0.037
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
0.23
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80%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
非表示の作業 76 件を表示| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Examine immigration applications, visas, and passports and interview persons to determine eligibility for admission, residence, and travel in the U.S.33-3021 | 0.052253.9 | 0.00627.7 |
Design, use, or maintain databases and software applications, such as geographic information systems (GIS) mapping and artificial intelligence tools.33-3021 | 0.00868.9 | 0.011314.0 |
Gather, analyze, correlate, or evaluate information from a variety of resources, such as law enforcement databases.33-3021 | 0.00707.2 | 0.00506.2 |
Prepare comprehensive written reports, presentations, maps, or charts based on research, collection, and analysis of intelligence data.33-3021 | 0.00656.7 | 0.014918.5 |
Gather intelligence information by field observation, confidential information sources, or public records.33-3021 | 0.00464.8 | 0.013917.3 |
Prepare charges or responses to charges, or information for court cases, according to formalized procedures.33-3021 | 0.00313.2 | —0 |
Manage security programs designed to protect personnel, facilities, and information.33-3021 | 0.00293.0 | 0.013817.1 |
Inspect cargo, baggage, and personal articles entering or leaving U.S. for compliance with revenue laws and U.S. customs regulations.33-3021 | 0.00242.5 | —0 |
Prepare reports that detail investigation findings.33-3021 | 0.00222.3 | —0 |
Interpret and explain laws and regulations to travelers, prospective immigrants, shippers, and manufacturers.33-3021 | 0.00212.2 | —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 |
|---|---|
Design, use, or maintain databases and software applications, such as geographic information systems (GIS) mapping and artificial intelligence tools.O*NET Task ID 17544 | 1.0 |
Study communication code languages or foreign languages to translate intelligence.O*NET Task ID 17559 | 1.0 |
Predict future gang, organized crime, or terrorist activity, using analyses of intelligence data.O*NET Task ID 17542 | 0.5 |
Study activities relating to narcotics, money laundering, gangs, auto theft rings, terrorism, or other national security threats.O*NET Task ID 17543 | 0.5 |
Establish criminal profiles to aid in connecting criminal organizations with their members.O*NET Task ID 17545 | 0.5 |
Evaluate records of communications, such as telephone calls, to plot activity and determine the size and location of criminal groups and members.O*NET Task ID 17546 | 0.5 |
Gather and evaluate information, using tools such as aerial photographs, radar equipment, or sensitive radio equipment.O*NET Task ID 17547 | 0.5 |
Gather intelligence information by field observation, confidential information sources, or public records.O*NET Task ID 17548 | 0.5 |
Gather, analyze, correlate, or evaluate information from a variety of resources, such as law enforcement databases.O*NET Task ID 17549 | 0.5 |
Link or chart suspects to criminal organizations or events to determine activities and interrelationships.O*NET Task ID 17550 | 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