情报分析师
保护服务AI暴露度
- 数据来源: BLS发布时间: 2026-08
高· 相对
低四级相对区间很高职业群单位数值
尺度 · 母数 · 来源
四级相对类别（低 / 中等 / 高 / 很高）
以BLS就业预测表中831个细分职业为基数。取值按 NEM（全国就业矩阵）代码给定，因此共用同一 NEM 代码的职业得到相同分档
- 数据来源: Anthropic发布时间: 2026-03
0.037
0.000此处所载数值的范围0.745尺度 · 母数 · 来源
- 数据来源: ILO发布时间: 2025
0.23
0.09此处所载数值的范围0.70职业群单位数值
尺度 · 母数 · 来源
生成式AI暴露度指数，按发布原值0–1
ISCO-08职业小类单位 — 共用同一代码的职业取值相同
本站计算，并非国际劳工组织发布的数值。在本站与 ILO 数据集相连的 1,012 个职业中，达到或高于此值的占 80%。
该来源发布的是什么性质的数值
BLS的类别是相对排位而非绝对水平，也不是一手测量：它把多项已发表研究给出的职业百分位排名归并为四个等级。它不是就业或工资预测，不是采用概率，也不区分自动化与增强。
Task-level exposure
显示隐藏的 83 项工作| 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 | 0.043354.5 | 0.00488.9 |
Design, use, or maintain databases and software applications, such as geographic information systems (GIS) mapping and artificial intelligence tools.33-3021 | 0.00729.1 | 0.010819.9 |
Prepare comprehensive written reports, presentations, maps, or charts based on research, collection, and analysis of intelligence data.33-3021 | 0.00546.8 | 0.007614.0 |
Gather, analyze, correlate, or evaluate information from a variety of resources, such as law enforcement databases.33-3021 | 0.00475.9 | 0.00387.0 |
Gather intelligence information by field observation, confidential information sources, or public records.33-3021 | 0.00374.7 | 0.010519.4 |
Analyze completed police reports to determine what additional information and investigative work is needed.33-3021 | 0.00303.8 | 0.00152.8 |
Manage security programs designed to protect personnel, facilities, and information.33-3021 | 0.00283.5 | 0.008115.0 |
Prepare charges or responses to charges, or information for court cases, according to formalized procedures.33-3021 | 0.00253.1 | —0 |
Prepare reports that detail investigation findings.33-3021 | 0.00182.3 | —0 |
Interpret and explain laws and regulations to travelers, prospective immigrants, shippers, and manufacturers.33-3021 | 0.00182.3 | —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