GCRI · 人工知能と自律システム
労働と社会への影響
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 システム 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.
モデル、その仮定、データ、不確実性を、科学者やエンジニアが確認する形で示します。構造は公表された手法に従っています。描かれた値は例示であり、知見ではありません。
システム・モデル
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.
Labour studies are designed for the Exponential Technology series. 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.
何を、どの程度うまく測定しているか。
各指標には、その単位、更新頻度、背後にある出典の種類、値が持つことになるエビデンスのラベルが付いています。値は表示しません：プログラムが自らの権限のもとで値を提供します。
| 指標 | 単位 | 周期 | 出典の種類 | ラベル |
|---|---|---|---|---|
| Employment in occupations highly exposed to AI | percent of employment | 評価ごと | Exposure studies using occupational data | 推定 |
| Firms using AI | percent of firms | 年次 | Business surveys | 報告値 |
| Workers using AI tools at work | percent of workers | per survey | Labour force and worker surveys | 報告値 |
| AI-related job postings | share of postings | 月次 | Online job posting data | 観測 |
| Participation in AI skills training | percent of workforce | 年次 | Training surveys | 報告値 |
| Wage changes in exposed occupations | percent change | 年次 | Wage statistics | 公表統計 |
エビデンスの出所。
名前の明示された公開データセットと機関の記録。それぞれにライセンス区分があります。合意に基づく出典は、その条件のもとでのみ使用し、再公開することはありません。
| 出典 | 提供するもの | ライセンス区分 |
|---|---|---|
| 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 |
誰が決め、エビデンスが何に役立つか。
人工知能と自律 システム prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| 誰が決定するか | 質問 | このページが提供するエビデンス |
|---|---|---|
| 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 |
| 教育 and training bodies | Which skills to teach and to whom | Skills demand evidence |
このトピックが生み出す成果物
- 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 社会・行動科学. 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
能力評価
Linking what システム can actually do, from AI 評価・保証工学, to claims about social effects.
どのように研究され、手法が言えないことは何か。
Where this page’s references lead.
このページの読解の基準となる機関と文書。状態と版は変わります。各リンクは発行機関へ移動します。
質問と回答。
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?
このページからエビデンスを提供する。
報告書 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.
- メールゲートウェイ GW-GCRIこのページから送信するエビデンス、訂正、事例、手法、レビュー、翻訳、プロファイルへの入力。このページのタグが付いて届くため、担当の編集者が受け取ります。
- Zenodoオープンな寄託寄託してコミュニティに提出するORCID iD を使って Zenodo に自分の記録を公開し、そこから Reports コミュニティに提出してください。キュレーターが審査し、DOI は引き続きあなたのものです。
- 報告書GCRI貢献のすべての方法を見る貢献の種類、それぞれの審査方法、受け取るクレジット、ライセンス、決して受け付けないもの。
個人データ、重要インフラの機微な詳細、共有する権利のないものは決して送らないでください。寄稿は利用前にレビューされます。送付によって著作者としての権利や編集上の権利が生じることはなく、スポンサーシップで調査結果を買うことは決してできません。
手法、データセット、または問いを持ち込んでください。
情報提供と問い合わせは本日から受け付けています。問い合わせには参照番号が付き、次の手順が示されます。申請、支払い、計算資源の割り当て、登録は、担当チーム、規約、審査体制が整ってから開始します。
法的文書。
このサイトの地図、データ、図表は、背景と一般的な情報として示すものです。助言、予報、警報ではありません。情報の限界は免責事項とデータに関する注意事項に記載しています。