信用咨询师
商业与金融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
- 数据来源: Anthropic发布时间: 2026-03
0.234
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
0.60
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 4%。
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 26 项工作| Task | Claude.aiRaw / share % |
|---|---|
Explain general financial topics to clients, such as credit report ratings, bankruptcy laws, consumer protection laws, wage attachments, or collection actions.13-2071 | 0.234339.3 |
Recommend strategies for clients to meet their financial goals, such as borrowing money through loans or loan programs, declaring bankruptcy, making budget adjustments, or enrolling in debt management plans.13-2071 | 0.072612.2 |
Create debt management plans, spending plans, or budgets to assist clients to meet financial goals.13-2071 | 0.03976.7 |
Counsel clients on personal and family financial problems, such as excessive spending or borrowing of funds.13-2071 | 0.03776.3 |
Explain services or policies to clients, such as debt management program rules, the advantages and disadvantages of using services, or creditor concession policies.13-2071 | 0.03756.3 |
Teach courses or seminars on topics such as budgeting, managing personal finances, or financial literacy.13-2071 | 0.03175.3 |
Prepare written documents to establish contracts with or communicate financial recommendations to clients.13-2071 | 0.03155.3 |
Calculate amount of debt and funds available to plan methods of payoff and to estimate time for debt liquidation.13-2071 | 0.02384.0 |
Recommend educational materials or resources to clients on matters such as financial planning, budgeting, or credit.13-2071 | 0.01863.1 |
Assist in selection of financial award candidates using electronic databases to certify loan eligibility.13-2071 | 0.01041.7 |
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 |
|---|---|
Calculate clients' available monthly income to meet debt obligations.O*NET Task ID 18935 | 1.0 |
Estimate time for debt repayment, given amount of debt, interest rates, and available funds.O*NET Task ID 18937 | 1.0 |
Explain services or policies to clients, such as debt management program rules, advantages and disadvantages of using services, or creditor concession policies.O*NET Task ID 18938 | 1.0 |
Prepare written documents to establish contracts with or communicate financial recommendations to clients.O*NET Task ID 18942 | 1.0 |
Explain general financial topics to clients, such as credit report ratings, bankruptcy laws, consumer protection laws, wage attachments, or collection actions.O*NET Task ID 18952 | 1.0 |
Explain loan information to clients, such as available loan types, eligibility requirements, or loan restrictions.O*NET Task ID 18953 | 1.0 |
Advise clients or respond to inquiries about financial matters in person or via phone, email, Web site, or Internet chat.O*NET Task ID 18933 | 0.5 |
Assess clients' overall financial situations by reviewing income, assets, debts, expenses, credit reports, or other financial information.O*NET Task ID 18934 | 0.5 |
Create debt management plans, spending plans, or budgets to assist clients to meet financial goals.O*NET Task ID 18936 | 0.5 |
Interview clients by telephone or in person to gather financial information.O*NET Task ID 18939 | 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