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AI暴露度

  • 数据来源: BLS发布时间: 2026-08

    中等· 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

    0.000

    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

    0.37

    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 53%。

    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 22 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
Complete order receipts.

O*NET Task ID 23871

1.0
Keep records on the use or damage of stock or stock-handling equipment.

O*NET Task ID 23894

1.0
Keep records of out-going orders.

O*NET Task ID 23895

1.0
Read orders to ascertain catalog numbers, sizes, colors, and quantities of merchandise.

O*NET Task ID 23873

0.5
Compare merchandise invoices to items actually received to ensure that shipments are correct.

O*NET Task ID 23881

0.5
Take inventory or examine merchandise to identify items to be reordered or replenished.

O*NET Task ID 23885

0.5
Design and set up advertising signs and displays of merchandise on shelves, counters, or tables to attract customers and promote sales.

O*NET Task ID 23889

0.5
Requisition merchandise from supplier, based on available space, merchandise on hand, customer demand, or advertised specials.

O*NET Task ID 23897

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