GCRI · Inteligencia artificial y sistemas autónomos
Efectos laborales y sociales
AI changes which tasks people do, how work is organised, which skills are valued and how information spreads. Effects differ by occupation, country, gender and age, and depend on how firms and governments choose to deploy it. This page sets out how AI affects work and society, how exposure and impacts are measured, and what evidence serves the labour ministries, employers, unions and educators involved.
Labour and social effects on the record.
Labour and social effects: how the risk develops.
Work, education, public trust and inclusion. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Tasks change before jobs do
AI automates or assists specific tasks within occupations. Jobs are reorganised around remaining tasks before they disappear or new ones form.
Exposure is uneven
Clerical and some professional tasks are highly exposed to generative AI, while manual and interpersonal work is less exposed. Women are over-represented in some highly exposed clerical jobs.
Skills and training lag
Workers need new skills to use AI well, and those displaced need retraining. Training sistemas often respond more slowly than technology adoption.
Information ecosystems shift
Generated content, recommendation systems and synthetic media change how people find and judge information, with effects on trust and public debate.
How labour and social effects is modelled, measured and tested.
El modelo, sus supuestos, sus datos y su incertidumbre, en la forma en que un científico o un ingeniero los comprobaría. Las estructuras siguen métodos publicados. Los valores representados son ilustrativos, no hallazgos.
Modelo de sistema
The couplings between the systems in labour and social effects: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
AI’s effects on work vary across domains.
- SaludAcoplamientoClinical and administrative tasks are exposed differently.What to watchTask exposure by occupation
- AlimentaciónAcoplamientoAgricultural work is less exposed to generative AI than clerical work.What to watchExposure by sector
- EnergíaAcoplamientoGrid operations need new digital skills.What to watchCarencias de competencias
Labour studies are designed for the serie Exponential Technology. The ILO’s 2023 working paper estimates exposure, not job loss, and finds clerical work most exposed.
- Firm-level adoption studies.
- Worker surveys on task change.
Qué se mide y con qué calidad.
Cada indicador lleva su unidad, su periodicidad, el tipo de fuente que lo respalda y la etiqueta de evidencia que llevaría un valor. No se muestran valores: un programa los aporta bajo su propia autoridad.
| Indicador | Unidad | Cadencia | Tipo de fuente | Etiqueta |
|---|---|---|---|---|
| Employment in occupations highly exposed to AI | percent of employment | por evaluación | Exposure studies using occupational data | ESTIMADO |
| Firms using AI | percent of firms | anual | Business surveys | NOTIFICADO |
| Workers using AI tools at work | percent of workers | per survey | Labour force and worker surveys | NOTIFICADO |
| AI-related job postings | share of postings | mensual | Online job posting data | OBSERVADO |
| Participation in AI skills training | percent of workforce | anual | Training surveys | NOTIFICADO |
| Wage changes in exposed occupations | percent change | anual | Wage statistics | Estadísticas publicadas |
De dónde procede la evidencia.
Conjuntos de datos públicos y registros institucionales identificados, cada uno con su tipo de licencia. Las fuentes obtenidas por acuerdo solo se utilizan conforme a sus condiciones y nunca se republican.
| Fuente | Lo que aporta | Tipo de licencia |
|---|---|---|
| ILO studies on generative AI exposure of occupations | Occupational exposure estimates by country and gender | Public, ILO terms |
| OECD Employment Outlook and AI in work studies | Evidence on AI adoption and labour effects | Public, OECD terms |
| Eurostat ICT usage in enterprises survey | Share of enterprises using AI | Open, Eurostat reuse policy |
| Online job posting data such as Lightcast summaries | Demand for AI skills | Commercial; public summaries (to check) |
| Stanford AI Index | Compiled indicators on AI adoption, investment and labour | Public, licence to check |
Quién decide y a qué sirve la evidencia.
Estudio de Inteligencia artificial y sistemas prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Quién decide | La pregunta | Evidencia a la que sirve esta página |
|---|---|---|
| Labour ministries | Which workers and sectors need transition support | Exposure analyses; adoption tracking |
| Employers and unions | How to introduce AI at work and share its gains | Adoption and wage evidence |
| Educación and training bodies | Which skills to teach and to whom | Skills demand evidence |
Productos que genera este tema
- Occupational exposure analyses by sector, gender and region
- Adoption tracking from business and worker surveys
- Evaluation designs for AI training programmes
- Evidence notes for social dialogue on AI at work
What researchers ask about social effects.
- Which tasks can current AI perform well, and how does that map onto occupations?
- Does AI substitute for workers, complement them or change what their jobs consist of?
- How does AI change learning and assessment in education?
- How do errors, opacity and misuse affect trust, and who is excluded?
Tasks, not jobs.
Jobs are bundles of tasks. AI tends to automate or assist specific tasks, such as drafting, summarising, coding or classifying images, which changes the content of jobs rather than simply removing them. Whether that raises productivity and wages or displaces workers depends on how organisations redesign work and how gains are shared.
In education, AI can personalise support but also undermine assessment and the learning that comes from effort. Trust depends on experience: visible errors, unexplained decisions and synthetic content erode it, while accountable use can build it. People with limited digital access, skills or language support risk exclusion.
Evidence on social effects.
- 01
Task exposure analysis
Mapping AI capabilities onto occupational task data. Exposure is not the same as displacement.
- 02
Field and natural experiments
Studying workplaces and classrooms that adopt AI, using ciencias sociales y del comportamiento. Early studies may not generalise.
- 03
Surveys of trust and use
Measuring how people use and perceive AI. Attitudes change quickly and vary by context.
- 04
Evaluación de capacidades
Linking what sistemas can actually do, from Ingeniería de evaluación y garantía de la IA, to claims about social effects.
Cómo se estudia y lo que los métodos no pueden decir.
- 01Ciencias sociales y del comportamientoEstudios de supervisión humana · Investigación con comunidades afectadas · Estudios de adopción en el lugar de trabajoAplicado aquí
- 02Probabilidad y estadísticaIncertidumbre de las puntuaciones de referencia · Evaluación de la calibración · Muestreo de auditoríaAplicado aquí
- 03Inteligencia artificial y aprendizaje automáticoAnálisis del cambio de distribución · Ajuste fino para un uso específico · Métodos de interpretabilidadAplicado aquí
- 04Robótica y automatizaciónPruebas basadas en escenarios · Seguridad de los robots colaborativos · Seguimiento del dominio operativoAplicado aquí
Where this page’s references lead.
Los organismos y documentos con los que se lee esta página. El estado y las ediciones cambian; cada enlace lleva al organismo emisor.
Preguntas y respuestas.
Will AI take jobs?
What is occupational exposure to AI?
Who is most exposed to generative AI?
Will AI cause mass unemployment?
Which workers are most exposed?
How does this topic relate to Future of Work and Skills?
Does the platform advise employers?
Aporte evidencia desde esta página.
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