GCRI · Kecerdasan Buatan dan Sistem Otonom
Dampak ketenagakerjaan dan sosial
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 sistem 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.
Model, asumsi, data, dan ketidakpastiannya, dalam bentuk yang akan diperiksa ilmuwan atau insinyur. Strukturnya mengikuti metode yang dipublikasikan. Nilai yang digambar bersifat ilustratif, bukan temuan.
Model sistem
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.
- KesehatanKeterkaitanClinical and administrative tasks are exposed differently.What to watchTask exposure by occupation
- PanganKeterkaitanAgricultural work is less exposed to generative AI than clerical work.What to watchExposure by sector
- EnergiKeterkaitanGrid operations need new digital skills.What to watchKesenjangan keterampilan
Labour studies are designed for the seri Teknologi Eksponensial. 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.
Apa yang diukur, dan seberapa baik.
Setiap indikator membawa satuan, frekuensi, jenis sumber di baliknya, dan label bukti yang akan dibawa suatu nilai. Tidak ada nilai yang ditampilkan: sebuah program menyediakannya berdasarkan wewenangnya sendiri.
| Indikator | Unit | Kadensi | Jenis sumber | Label |
|---|---|---|---|---|
| Employment in occupations highly exposed to AI | percent of employment | per penilaian | Exposure studies using occupational data | ESTIMASI |
| Firms using AI | percent of firms | tahunan | Business surveys | DILAPORKAN |
| Workers using AI tools at work | percent of workers | per survey | Labour force and worker surveys | DILAPORKAN |
| AI-related job postings | share of postings | setiap bulan | Online job posting data | TERAMATI |
| Participation in AI skills training | percent of workforce | tahunan | Training surveys | DILAPORKAN |
| Wage changes in exposed occupations | percent change | tahunan | Wage statistics | Statistik terbitan |
Asal bukti.
Set data publik bernama dan catatan kelembagaan, masing-masing dengan kelas lisensinya. Sumber berdasarkan perjanjian hanya digunakan sesuai ketentuannya dan tidak pernah diterbitkan ulang.
| Sumber | Apa yang diberikannya | Kelas lisensi |
|---|---|---|
| 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 |
Siapa yang memutuskan, dan apa yang dilayani bukti.
Studio Kecerdasan Buatan dan Sistem prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Siapa yang memutuskan | Pertanyaannya | Bukti yang dilayani halaman ini |
|---|---|---|
| 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 |
| Pendidikan and training bodies | Which skills to teach and to whom | Skills demand evidence |
Keluaran yang dihasilkan topik ini
- 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 ilmu sosial dan perilaku. 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
Evaluasi kapabilitas
Linking what sistem can actually do, from Evaluasi AI dan rekayasa penjaminan, to claims about social effects.
Cara hal ini dikaji, dan apa yang tidak dapat dinyatakan metodenya.
- 01Ilmu sosial dan perilakuKajian pengawasan manusia · Riset masyarakat terdampak · Kajian adopsi di tempat kerjaDiterapkan di sini
- 02Probabilitas dan statistikaKetidakpastian pada skor tolok ukur · Penilaian kalibrasi · Sampel auditDiterapkan di sini
- 03Kecerdasan buatan dan pembelajaran mesinAnalisis pergeseran distribusi · Fine-tuning untuk penggunaan tertentu · Metode interpretabilitasDiterapkan di sini
- 04Robotika dan otomasiPengujian berbasis skenario · Keselamatan robot kolaboratif · Pemantauan domain operasiDiterapkan di sini
Where this page’s references lead.
Badan dan dokumen yang menjadi acuan halaman ini. Status dan edisi berubah; setiap tautan menuju badan penerbitnya.
Tanya jawab.
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?
Kontribusikan bukti dari halaman ini.
Laporan is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
- SurelGerbang GW-GCRIKirim dari halaman iniBukti, koreksi, kasus, metode, tinjauan, terjemahan, atau masukan profil. Kiriman tiba dengan label halaman ini, sehingga editor pemiliknya menerimanya.
- ZenodoDeposit terbukaDeposit dan ajukan ke komunitasTerbitkan catatan Anda sendiri di Zenodo dengan ORCID iD Anda, lalu ajukan ke komunitas Reports di sana. Kurator meninjaunya; DOI tetap milik Anda.
- LaporanGCRILihat semua cara berkontribusiJenis kontribusi, cara masing-masing ditinjau, kredit yang Anda terima, lisensinya, dan apa yang tidak pernah diterima.
Jangan pernah mengirim data pribadi, rincian infrastruktur sensitif, atau apa pun yang tidak berhak Anda bagikan. Kontribusi ditinjau sebelum digunakan. Mengirimkannya tidak memberikan hak kepenulisan atau editorial, dan sponsor tidak pernah membeli sebuah temuan.
Bawa metode, dataset, atau pertanyaan.
Informasi dan pertanyaan sudah terbuka hari ini. Sebuah pertanyaan menerima nomor referensi dan menyebutkan langkah berikutnya; lamaran, pembayaran, alokasi komputasi, dan pendaftaran hanya dibuka ketika tim, ketentuan, dan kapasitas tinjauannya tersedia.
Dokumen hukum.
Peta, data, dan gambar di situs ini ditampilkan untuk konteks dan informasi umum. Semua itu bukan nasihat, prakiraan, atau peringatan; batasan informasinya dijelaskan dalam Penafian dan Pemberitahuan Data.