保险承保人

商业与金融

AI暴露度

  • 数据来源: BLS发布时间: '26.08

    很高· 相对

    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

    来源数据集(XLSX 文件下载)

  • 数据来源: Anthropic发布时间: '26.03

    0.063

    0.000此处所载数值的范围0.745
    尺度 · 母数 · 来源

    观测暴露度指数,按发布原值0–1

    以O*NET任务映射为基准

    来源数据集

  • 数据来源: ILO发布时间: '25

    0.53

    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 14%。

    来源数据集

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

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.

TaskβE1 + 0.5 × E2
Write to field representatives, medical personnel, or others to obtain further information, quote rates, or explain company underwriting policies.

O*NET Task ID 1262

1.0
Decline excessive risks.

O*NET Task ID 1261

0.5
Evaluate possibility of losses due to catastrophe or excessive insurance.

O*NET Task ID 1263

0.5
Decrease value of policy when risk is substandard and specify applicable endorsements or apply rating to ensure safe, profitable distribution of risks, using reference materials.

O*NET Task ID 1264

0.5
Review company records to determine amount of insurance in force on single risk or group of closely related risks.

O*NET Task ID 1265

0.5
Authorize reinsurance of policy when risk is high.

O*NET Task ID 1266

0.5
Examine documents to determine degree of risk from factors such as applicant health, financial standing and value, and condition of property.

O*NET Task ID 21045

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®, Eloundou et al. (2023): see full notices on the Credits page

职业信息

与本职业相关的近期变化

2026年4月: ATE 0.42 by 2027 in SF Bay Tier 1. Rule-application heavy roles with thin P1 (interpersonal) and P2 (regulatory) penalties show fast agentic exposure climb.

[来源: arXiv 2604.00186 (Gupta & Kumar, 2026)]

2026年3月: Published evergreen blog analysis: AI exposure 64%, automation risk 62/100 in 2025.

[来源: AI Changing Work Blog]