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
露出のある作業のみ| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Prepare real estate closing statements, using knowledge and expertise in real estate procedures.23-2093 | 0.019564.8 | 0.004314.0 |
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 | 0.005618.6 | 0.009129.5 |
Prepare reports describing any title encumbrances encountered during searching activities, and outlining actions needed to clear titles.23-2093 | 0.00289.3 | 0.00154.9 |
Confer with realtors, lending institution personnel, buyers, sellers, contractors, surveyors, and courthouse personnel to exchange title-related information or to resolve problems.23-2093 | 0.00227.3 | 0.00196.2 |
Copy or summarize recorded documents, such as mortgages, trust deeds, and contracts, that affect property titles. | —0 | 0.008828.6 |
Enter into record-keeping systems appropriate data needed to create new title records or update existing ones. | —0 | 0.003511.4 |
Examine individual titles to determine if restrictions, such as delinquent taxes, will affect titles and limit property use. | —0 | 0.00175.5 |
| Not observed on any surface — 10 task(s) — These tasks have no row in the source for this release. The 0 in the share row is a display-stage composition ratio; absence is what the — in the raw row states. | ||
Prepare lists of all legal instruments applying to a specific piece of land and the buildings on it. | —0 | —0 |
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. | —0 | —0 |
Read search requests to ascertain types of title evidence required and to obtain descriptions of properties and names of involved parties. | —0 | —0 |
Verify accuracy and completeness of land-related documents accepted for registration, preparing rejection notices when documents are not acceptable. | —0 | —0 |
Direct activities of workers who search records and examine titles, assigning, scheduling, and evaluating work, and providing technical guidance as necessary. | —0 | —0 |
Obtain maps or drawings delineating properties from company title plants, county surveyors, or assessors' offices. | —0 | —0 |
Prepare and issue title commitments and title insurance policies based on information compiled from title searches. | —0 | —0 |
Retrieve and examine real estate closing files for accuracy and to ensure that information included is recorded and executed according to regulations. | —0 | —0 |
Determine whether land-related documents can be registered under the relevant legislation such as the Land Titles Act. | —0 | —0 |
Assess fees related to registration of property-related documents. | —0 | —0 |
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 |
Confer with realtors, lending institution personnel, buyers, sellers, contractors, surveyors, and courthouse personnel to exchange title-related information or to resolve problems.O*NET Task ID 9237 | 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®, 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]