Human Factors Engineers and Ergonomists
Architecture and Engineering Occupations- Código O*NET-SOC
- 17-2112.01
Design objects, facilities, and environments to optimize human well-being and overall system performance, applying theory, principles, and data regarding the relationship between humans and respective technology. Investigate and analyze characteristics of human behavior and performance as it relates to the use of technology.
Os nomes das ocupações e as descrições de tarefas são exibidos em inglês, tal como publicados. As etiquetas, incluindo os tipos de tarefa, estão traduzidas.
Exposição à IA
- Fonte dos dados: BLSPublicado: 2026-08
Muito alto· relativa
BaixoQuatro faixas relativasMuito altoValor por grupo ocupacional
Escala, base e fonte
Quatro faixas relativas (Baixo / Moderado / Alto / Muito alto)
831 ocupações detalhadas da tabela de projeções de emprego do BLS. O valor é atribuído por código da National Employment Matrix (NEM), pelo que as ocupações que partilham um código NEM recebem a mesma banda
- Fonte dos dados: AnthropicPublicado: 2026-03
0.037
0.000Intervalo dos valores aqui apresentados0.745Valor por grupo ocupacional
Escala, base e fonte
Índice de exposição observada, 0–1 tal como publicado
Mapeado sobre tarefas O*NET
Publicado por ocupação SOC 2018; toda ocupação O*NET com o mesmo código SOC 2018 recebe este valor
- Fonte dos dados: ILOPublicado: 2025
0.37
0.09Intervalo dos valores aqui apresentados0.70Valor por grupo ocupacional
Escala, base e fonte
Índice de exposição à IA generativa, 0–1 tal como publicado
Publicado por grupo de base ISCO-08. Ligado a esta ocupação, total ou parcialmente, aplicando tal como publicadas as tabelas de correspondência do U.S. Bureau of Labor Statistics (ISCO-08 para SOC 2010, SOC 2010 para SOC 2018)
Que tipo de valor esta fonte publica
A categoria de BLS é uma posição relativa, não um nível absoluto, e também não é uma medição de primeira mão: agrupa em quatro faixas as posições percentis da ocupação em vários estudos publicados. Não é uma previsão de emprego ou de salários, não é uma probabilidade de adoção e não distingue automação de aumento.
Exposição à IA (rubrica da OpenAI)
26 tarefas avaliadas · 23 tarefas com β ≥ 0,5 (88.5%)
β = exposição direta (E1) + 0,5 × exposição com ferramentas disponíveis (E2), segundo a definição do repositório de origem.
- Unidade de origem: tarefas do O*NET 27.2 → código de ocupação do O*NET 31.0
- Todas as tarefas pontuadas constam da lista de tarefas do O*NET 31.0.
- Fonte
- OpenAI "GPTs are GPTs" exposure rubric
- Versão
- gh-main-0471612
- Licença
- MIT License, Copyright (c) 2024 OpenAI
Tarefas
Descrições de tarefas da O*NET® 31.0 Database, com as tarefas principais primeiro.
| Tarefa | Tipo | β (OpenAI) |
|---|---|---|
| Write, review, or comment on documents, such as proposals, test plans, or procedures. | Principal | 1 |
| Train users in task techniques or ergonomic principles. | Principal | 0 |
| Review health, safety, accident, or worker compensation records to evaluate safety program effectiveness or to identify jobs with high incidence of injury. | Principal | 0,5 |
| Provide human factors technical expertise on topics, such as advanced user-interface technology development or the role of human users in automated or autonomous sub-systems in advanced vehicle systems. | Principal | 0,5 |
| Investigate theoretical or conceptual issues, such as the human design considerations of lunar landers or habitats. | Principal | 0,5 |
| Estimate time or resource requirements for ergonomic or human factors research or development projects. | Principal | 0,5 |
| Conduct interviews or surveys of users or customers to collect information on topics, such as requirements, needs, fatigue, ergonomics, or interfaces. | Principal | 1 |
| Recommend workplace changes to improve health and safety, using knowledge of potentially harmful factors, such as heavy loads or repetitive motions. | Principal | 0,5 |
| Provide technical support to clients through activities, such as rearranging workplace fixtures to reduce physical hazards or discomfort or modifying task sequences to reduce cycle time. | Principal | 0,5 |
| Prepare reports or presentations summarizing results or conclusions of human factors engineering or ergonomics activities, such as testing, investigation, or validation. | Principal | 1 |
| Perform statistical analyses, such as social network pattern analysis, network modeling, discrete event simulation, agent-based modeling, statistical natural language processing, computational sociology, mathematical optimization, or systems dynamics. | Principal | 1 |
| Perform functional, task, or anthropometric analysis, using tools, such as checklists, surveys, videotaping, or force measurement. | Principal | 0,5 |
| Operate testing equipment, such as heat stress meters, octave band analyzers, motion analysis equipment, inclinometers, light meters, thermoanemometers, sling psychrometers, or colorimetric detection tubes. | Principal | 0 |
| Integrate human factors requirements into operational hardware. | Principal | 0 |
| Establish system operating or training requirements to ensure optimized human-machine interfaces. | Principal | 0,5 |
| Inspect work sites to identify physical hazards. | Principal | 0,5 |
| Develop or implement human performance research, investigation, or analysis protocols. | Principal | 0,5 |
| Develop or implement research methodologies or statistical analysis plans to test and evaluate developmental prototypes used in new products or processes, such as cockpit designs, user workstations, or computerized human models. | Principal | 0,5 |
| Design cognitive aids, such as procedural storyboards or decision support systems. | Principal | 0,5 |
| Conduct research to evaluate potential solutions related to changes in equipment design, procedures, manpower, personnel, or training. | Principal | 0,5 |
| Assess the user-interface or usability characteristics of products. | Principal | 0,5 |
| Collect data through direct observation of work activities or witnessing the conduct of tests. | Principal | 0,5 |
| Apply modeling or quantitative analysis to forecast events, such as human decisions or behaviors, the structure or processes of organizations, or the attitudes or actions of human groups. | Principal | 0,5 |
| Analyze complex systems to determine potential for further development, production, interoperability, compatibility, or usefulness in a particular area, such as aviation. | Principal | 0,5 |
| Advocate for end users in collaboration with other professionals, including engineers, designers, managers, or customers. | Principal | 0,5 |
| Design or evaluate human work systems, using human factors engineering and ergonomic principles to optimize usability, cost, quality, safety, or performance. | Principal | 0,5 |
Informação ocupacional
Fontes e atribuição
This page includes information from the O*NET® 31.0 Database (https://www.onetcenter.org/database.html) by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/). O*NET® is a trademark of USDOL/ETA. AI Changing Work has modified all or some of this information: the O*NET-SOC code, title and task statements are reproduced in English without change; task-type labels are shown in the page's language and tasks are listed core first; any Korean occupation title shown on the Korean-language page is AI Changing Work's translation; any KSCO-8 unit groups linked to this occupation were paired with it by AI Changing Work's judgment, and the relation labels and statuses are AI Changing Work's additions. USDOL/ETA has not approved, endorsed, or tested these modifications.
Any AI exposure figures on this page are published by third parties, not by AI Changing Work, and none is part of the O*NET information. OpenAI publishes task-level scores (MIT License) for O*NET 27.2 task statements; each is shown next to the O*NET 31.0 task statement with the same task ID, whose wording can differ from the 27.2 statement that was scored. OpenAI also publishes occupation-level scores for O*NET-SOC codes in the same release, and any such score is shown on the O*NET occupation with the same code. Anthropic publishes an observed exposure index in the Anthropic Economic Index (CC-BY), and the U.S. Bureau of Labor Statistics publishes relative AI exposure categories (public domain); both are published per SOC code, and each value is shown on every O*NET occupation with that code. The International Labour Organization publishes a generative AI exposure index in ILO Working Paper 140 (CC BY 4.0) for ISCO-08 unit groups; AI Changing Work links those groups to O*NET occupations by applying the U.S. Bureau of Labor Statistics ISCO-08 to 2010 SOC and 2010 SOC to 2018 SOC crosswalks as published, without case-by-case selection, and these crosswalks match many groups only in part. Where several unit groups are linked, each group's published value is listed, and any summary shows only the lowest and highest of those values with the number of groups; no exposure figure is averaged or recalculated. Any employment figures are published by the U.S. Bureau of Labor Statistics for the SOC group containing this occupation. Each source is credited where its figures are shown.
O*NET OnLine: 17-2112.01 Human Factors Engineers and Ergonomists
KSCO 코드·명칭: 한국표준직업분류(제8차 개정) — 통계청 고시 제2024-328호 (2024-07-01 고시, 2025-01-01 시행). 저작권법 제7조 제2호의 고시 항목이다. 명칭 표기(가운뎃점·띄어쓰기)는 해설서 2차 정오 반영판의 표기를 따랐으며, 고시 항목표와는 18개 명칭에서 가운뎃점 글리프나 띄어쓰기만 다르다. 통계청은 2025년 10월 국가데이터처로 개편되었다. 이 페이지의 KSCO 연결은 통계청·국가데이터처의 공식 연계표가 아니다.