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AI露出度

  • データ出典: BLS公表時点: '26.08

    非常に高い· 相対

    低い4段階の相対区分非常に高い

    職業群単位の値

    尺度・母数・出典

    4段階の相対区分(低い / 中程度 / 高い / 非常に高い)

    BLS雇用見通し表の詳細職業831件が母数。値は NEM(全国雇用マトリクス)コード単位で付与されるため、同じ NEM コードの職業は同じバンドになります

    出典データセット(XLSX ファイルのダウンロード)

  • データ出典: Anthropic公表時点: '26.03

    0.701

    0.000ここに掲載された値の範囲0.745
    尺度・母数・出典

    観測エクスポージャー指数、公開されたまま0–1

    O*NETタスクへの対応づけが基準

    出典データセット

  • データ出典: ILO公表時点: '25

    0.58

    0.09ここに掲載された値の範囲0.70

    職業群単位の値

    尺度・母数・出典

    生成AI露出度指数、公開されたまま0–1

    ISCO-08の職業小分類単位 — 同じコードの職業はすべて同じ値

    当サイトの算出であり、ILOが公表した数値ではありません。当サイトがILOデータセットに結び付けた職業1,012件のうち、この値以上のものは5%です。

    出典データセット

この出典がどのような性格の値か

BLSの区分は絶対水準ではなく相対順位であり、一次測定でもありません。複数の既存研究が付けた職業別パーセンタイル順位を4段階にまとめた値です。雇用や賃金の予測でもなく、導入確率でもなく、自動化と増強を区別しません。

Task-level exposure

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.

TaskβE1 + 0.5 × E2
Complete contract forms, prepare change of address records, or issue service discontinuance orders, using computers.

O*NET Task ID 2584

1.0
Compare disputed merchandise with original requisitions and information from invoices and prepare invoices for returned goods.

O*NET Task ID 2589

1.0
Keep records of customer interactions or transactions, recording details of inquiries, complaints, or comments, as well as actions taken.

O*NET Task ID 2578

0.5
Resolve customers' service or billing complaints by performing activities such as exchanging merchandise, refunding money, or adjusting bills.

O*NET Task ID 2579

0.5
Check to ensure that appropriate changes were made to resolve customers' problems.

O*NET Task ID 2580

0.5
Contact customers to respond to inquiries or to notify them of claim investigation results or any planned adjustments.

O*NET Task ID 2581

0.5
Refer unresolved customer grievances to designated departments for further investigation.

O*NET Task ID 2582

0.5
Determine charges for services requested, collect deposits or payments, or arrange for billing.

O*NET Task ID 2583

0.5
Obtain and examine all relevant information to assess validity of complaints and to determine possible causes, such as extreme weather conditions that could increase utility bills.

O*NET Task ID 2585

0.5
Solicit sales of new or additional services or products.

O*NET Task ID 2586

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®, Eloundou et al. (2023): see full notices on the Credits page

Occupation information

この職業に関わる最近の変化

2026年6月: Cited by ADP Research as an example of a high-AI-exposure occupation. Group-level payroll data shows employment in high-exposure occupations down 0.2% year over year overall and down 4.3% for workers aged 22-25 (33rd consecutive monthly decline). The percentages are for the high-exposure group, not for this occupation alone.

[出典: ADP Research, Canaries Dashboard (June 2026)]

2026年5月: Anthropic observed-exposure score: high (zero->observed gap small). High observed exposure due to simpler software pipelines and lower integration cost.

[出典: Anthropic Economic Research (Massenkoff & McCrory, 2026)]

2026年4月: NBER survey of 6,000 executives: 69% use AI but 90% report zero employment impact. Executives predict -0.7% employment decline over next 3 years.

[出典: NBER Working Paper 34836]