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食品調理・サービス

AI露出度

  • データ出典: BLS公表時点: 2026-08

    中程度· 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: 2026-03

    0.000

    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: 2025

    0.28

    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73%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

露出のある作業のみ

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
Write patrons' food orders on order slips, memorize orders, or enter orders into computers for transmittal to kitchen staff.

O*NET Task ID 2277

1.0
Explain how various menu items are prepared, describing ingredients and cooking methods.

O*NET Task ID 2287

1.0
Prepare checks that itemize and total meal costs and sales taxes.

O*NET Task ID 2281

0.5
Present menus to patrons and answer questions about menu items, making recommendations upon request.

O*NET Task ID 2283

0.5
Inform customers of daily specials.

O*NET Task ID 2284

0.5
Describe and recommend wines to customers.

O*NET Task ID 2294

0.5
Provide guests with information about local areas, including directions.

O*NET Task ID 18748

0.5
Check patrons' identification to ensure that they meet minimum age requirements for consumption of alcoholic beverages.

O*NET Task ID 2275

0.0
Collect payments from customers.

O*NET Task ID 2276

0.0
Take orders from patrons for food or beverages.

O*NET Task ID 2278

0.0
Check with customers to ensure that they are enjoying their meals, and take action to correct any problems.

O*NET Task ID 2279

0.0
Serve food or beverages to patrons, and prepare or serve specialty dishes at tables as required.

O*NET Task ID 2280

0.0
Clean tables or counters after patrons have finished dining.

O*NET Task ID 2285

0.0
Prepare hot, cold, and mixed drinks for patrons, and chill bottles of wine.

O*NET Task ID 2286

0.0
Prepare tables for meals, including setting up items such as linens, silverware, and glassware.

O*NET Task ID 2288

0.0
Perform food preparation duties, such as preparing salads, appetizers, and cold dishes, portioning desserts, and brewing coffee.

O*NET Task ID 2289

0.0
Stock service areas with supplies such as coffee, food, tableware, and linens.

O*NET Task ID 2290

0.0
Garnish and decorate dishes in preparation for serving.

O*NET Task ID 2291

0.0
Fill salt, pepper, sugar, cream, condiment, and napkin containers.

O*NET Task ID 2292

0.0
Escort customers to their tables.

O*NET Task ID 2293

0.0
Bring wine selections to tables with appropriate glasses, and pour the wines for customers.

O*NET Task ID 2295

0.0
Roll silverware, set up food stations, or set up dining areas to prepare for the next shift or for large parties.

O*NET Task ID 18744

0.0
Remove dishes and glasses from tables or counters, and take them to kitchen for cleaning.

O*NET Task ID 18745

0.0
Assist host or hostess by answering phones to take reservations or to-go orders, and by greeting, seating, and thanking guests.

O*NET Task ID 18746

0.0
Perform cleaning duties, such as sweeping and mopping floors, vacuuming carpet, tidying up server station, taking out trash, or checking and cleaning bathroom.

O*NET Task ID 18747

0.0

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

職業情報