人力资源经理
管理AI暴露度
- 数据来源: BLS发布时间: '26.08
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: '26.03
0.000
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: '25
0.36
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 60%。
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 17 项工作| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Provide current and prospective employees with information about policies, job duties, working conditions, wages, opportunities for promotion and employee benefits.11-3121 | 0.013330.3 | 0.019959.9 |
Develop, administer and evaluate applicant tests.11-3121 | 0.012328.0 | 0.004112.3 |
Analyze and modify compensation and benefits policies to establish competitive programs and ensure compliance with legal requirements.11-3121 | 0.006414.6 | 0.00164.8 |
Identify staff vacancies and recruit, interview and select applicants.11-3121 | 0.00214.8 | 0.00226.6 |
Analyze training needs to design employee development, language training and health and safety programs.11-3121 | 0.00214.8 | 0.00154.5 |
Analyze statistical data and reports to identify and determine causes of personnel problems and develop recommendations for improvement of organization's personnel policies and practices.11-3121 | 0.00204.6 | 0.00247.2 |
Advise managers on organizational policy matters such as equal employment opportunity and sexual harassment, and recommend needed changes.11-3121 | 0.00204.6 | —0 |
Maintain records and compile statistical reports concerning personnel-related data such as hires, transfers, performance appraisals, and absenteeism rates.11-3121 | 0.00194.3 | 0.00154.5 |
Serve as a link between management and employees by handling questions, interpreting and administering contracts and helping resolve work-related problems.11-3121 | 0.00184.1 | —0 |
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 to identify reasons for employee termination.O*NET Task ID 996 | 1.0 |
Administer compensation, benefits, and performance management systems, and safety and recreation programs.O*NET Task ID 982 | 0.5 |
Identify staff vacancies and recruit, interview, and select applicants.O*NET Task ID 983 | 0.5 |
Allocate human resources, ensuring appropriate matches between personnel.O*NET Task ID 984 | 0.5 |
Provide current and prospective employees with information about policies, job duties, working conditions, wages, opportunities for promotion, and employee benefits.O*NET Task ID 985 | 0.5 |
Perform difficult staffing duties, including dealing with understaffing, refereeing disputes, firing employees, and administering disciplinary procedures.O*NET Task ID 986 | 0.5 |
Advise managers on organizational policy matters, such as equal employment opportunity and sexual harassment, and recommend needed changes.O*NET Task ID 987 | 0.5 |
Analyze and modify compensation and benefits policies to establish competitive programs and ensure compliance with legal requirements.O*NET Task ID 988 | 0.5 |
Serve as a link between management and employees by handling questions, interpreting and administering contracts and helping resolve work-related problems.O*NET Task ID 990 | 0.5 |
Plan, direct, supervise, and coordinate work activities of subordinates and staff relating to employment, compensation, labor relations, and employee relations.O*NET Task ID 991 | 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