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AI暴露度

  • 数据来源: BLS发布时间: 2026-08

    很高· 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

    来源数据集（XLSX 文件下载）

  • 数据来源: Anthropic发布时间: 2026-03

    0.130

    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: 2025

    0.64

    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 1%。

    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

显示隐藏的 1 项工作

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
Keep records of customers' charges and payments.

O*NET Task ID 23297

1.0
File sales slips in customers' ledgers for billing purposes.

O*NET Task ID 23302

1.0
Receive charge slips or credit applications by mail, or receive information from salespeople or merchants by telephone.

O*NET Task ID 23303

1.0
Mail charge statements to customers.

O*NET Task ID 23304

1.0
Relay credit report information to subscribers by mail or by telephone.

O*NET Task ID 23306

1.0
Prepare reports of findings and recommendations.

O*NET Task ID 23311

1.0
Compile and analyze credit information gathered by investigation.

O*NET Task ID 23298

0.5
Obtain information about potential creditors from banks, credit bureaus, and other credit services, and provide reciprocal information if requested.

O*NET Task ID 23299

0.5
Interview credit applicants by telephone or in person to obtain personal and financial data needed to complete credit report.

O*NET Task ID 23300

0.5
Evaluate customers' computerized credit records and payment histories to decide whether to approve new credit, based on predetermined standards.

O*NET Task ID 23301

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