権原審査官
法律AI露出度
- データ出典: BLS公表時点: 2026-08
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
- データ出典: Anthropic公表時点: 2026-03
0.022
0.000ここに掲載された値の範囲0.745尺度・母数・出典
- データ出典: ILO公表時点: 2025
0.39
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44%です。
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
非表示の作業 13 件を表示| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Prepare real estate closing statements, using knowledge and expertise in real estate procedures.23-2093 | 0.006750.0 | —0 |
Summarize pertinent legal or insurance details, or sections of statutes or case law from reference books so that they can be used in examinations, or as proofs or ready reference.23-2093 | 0.004835.4 | 0.005251.6 |
Copy or summarize recorded documents, such as mortgages, trust deeds, and contracts, that affect property titles.23-2093 | 0.002014.6 | 0.003029.5 |
Enter into record-keeping systems appropriate data needed to create new title records or update existing ones. | —0 | 0.001918.9 |
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.
| Task | βE1 + 0.5 × E2 |
|---|---|
Read search requests to ascertain types of title evidence required and to obtain descriptions of properties and names of involved parties.O*NET Task ID 9232 | 1.0 |
Copy or summarize recorded documents, such as mortgages, trust deeds, and contracts, that affect property titles.O*NET Task ID 9233 | 1.0 |
Enter into record-keeping systems appropriate data needed to create new title records or to update existing ones.O*NET Task ID 9238 | 1.0 |
Prepare lists of all legal instruments applying to a specific piece of land and the buildings on it.O*NET Task ID 9230 | 0.5 |
Examine documentation such as mortgages, liens, judgments, easements, plat books, maps, contracts, and agreements to verify factors such as properties' legal descriptions, ownership, or restrictions.O*NET Task ID 9231 | 0.5 |
Examine individual titles to determine if restrictions, such as delinquent taxes, will affect titles and limit property use.O*NET Task ID 9234 | 0.5 |
Prepare reports describing any title encumbrances encountered during searching activities and outlining actions needed to clear titles.O*NET Task ID 9235 | 0.5 |
Verify accuracy and completeness of land-related documents accepted for registration, preparing rejection notices when documents are not acceptable.O*NET Task ID 9236 | 0.5 |
Direct activities of workers who search records and examine titles, assigning, scheduling, and evaluating work, and providing technical guidance as necessary.O*NET Task ID 9239 | 0.5 |
Obtain maps or drawings delineating properties from company title plants, county surveyors, or assessors' offices.O*NET Task ID 9240 | 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®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page
職業情報
この職業に関わる最近の変化
2026年3月: New blog post: AI impact analysis for title agents
[出典: aichanging.work]2026年3月: Published evergreen blog post analyzing AI impact on title examiners (62% automation risk, 67% exposure)
[出典: AI Changing Work Blog]