人力资源专家
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
0.403
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
0.45
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 25%。
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 11 项工作| Task | Claude.aiRaw / share % |
|---|---|
Contact job applicants to inform them of the status of their applications.13-1071 | 0.037426.4 |
Interpret and explain human resources policies, procedures, laws, standards, or regulations.13-1071 | 0.022315.7 |
Evaluate recruitment or selection criteria to ensure conformance to professional, statistical, or testing standards, recommending revisions, as needed.13-1071 | 0.020714.6 |
Review employment applications and job orders to match applicants with job requirements.13-1071 | 0.014910.5 |
Perform searches for qualified job candidates, using sources such as computer databases, networking, Internet recruiting resources, media advertisements, job fairs, recruiting firms, or employee referrals.13-1071 | 0.01027.2 |
Interview job applicants to obtain information on work history, training, education, or job skills.13-1071 | 0.00835.9 |
Maintain and update human resources documents, such as organizational charts, employee handbooks or directories, or performance evaluation forms.13-1071 | 0.00483.4 |
Analyze employment-related data and prepare required reports.13-1071 | 0.00433.0 |
Maintain current knowledge of Equal Employment Opportunity (EEO) and affirmative action guidelines and laws, such as the Americans with Disabilities Act (ADA).13-1071 | 0.00422.9 |
Provide management with information or training related to interviewing, performance appraisals, counseling techniques, or documentation of performance issues.13-1071 | 0.00422.9 |
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 |
|---|---|
Conduct exit interviews and ensure that necessary employment termination paperwork is completed.O*NET Task ID 18854 | 1.0 |
Contact job applicants to inform them of the status of their applications.O*NET Task ID 18857 | 1.0 |
Inform job applicants of details such as duties and responsibilities, compensation, benefits, schedules, working conditions, or promotion opportunities.O*NET Task ID 18860 | 1.0 |
Interpret and explain human resources policies, procedures, laws, standards, or regulations.O*NET Task ID 18861 | 1.0 |
Maintain and update human resources documents, such as organizational charts, employee handbooks or directories, or performance evaluation forms.O*NET Task ID 18863 | 1.0 |
Maintain current knowledge of Equal Employment Opportunity (EEO) and affirmative action guidelines and laws, such as the Americans with Disabilities Act (ADA).O*NET Task ID 18864 | 1.0 |
Prepare or maintain employment records related to events, such as hiring, termination, leaves, transfers, or promotions, using human resources management system software.O*NET Task ID 18866 | 1.0 |
Review and evaluate applicant qualifications or eligibility for specified licensing, according to established guidelines and designated licensing codes.O*NET Task ID 18876 | 1.0 |
Analyze employment-related data and prepare required reports.O*NET Task ID 18853 | 0.5 |
Conduct reference or background checks on job applicants.O*NET Task ID 18855 | 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