裁判官
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
0.311
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
0.31
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65%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 20 件を表示| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Read documents on pleadings and motions to ascertain facts and issues.23-1023 | 0.0100100.0 | 0.0200100.0 |
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 |
|---|---|
Sentence defendants in criminal cases, on conviction by jury, according to applicable government statutes.O*NET Task ID 5644 | 0.5 |
Rule on admissibility of evidence and methods of conducting testimony.O*NET Task ID 5645 | 0.5 |
Preside over hearings and listen to allegations made by plaintiffs to determine whether the evidence supports the charges.O*NET Task ID 5646 | 0.5 |
Read documents on pleadings and motions to ascertain facts and issues.O*NET Task ID 5647 | 0.5 |
Interpret and enforce rules of procedure or establish new rules in situations where there are no procedures already established by law.O*NET Task ID 5648 | 0.5 |
Monitor proceedings to ensure that all applicable rules and procedures are followed.O*NET Task ID 5649 | 0.5 |
Advise attorneys, juries, litigants, and court personnel regarding conduct, issues, and proceedings.O*NET Task ID 5650 | 0.5 |
Research legal issues and write opinions on the issues.O*NET Task ID 5651 | 0.5 |
Conduct preliminary hearings to decide issues, such as whether there is reasonable and probable cause to hold defendants in felony cases.O*NET Task ID 5652 | 0.5 |
Write decisions on cases.O*NET Task ID 5653 | 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: Same paper assigns ATE 0.43 by 2027 in SF Bay Tier 1. 100% of legal occupations in scope cross moderate-risk threshold by 2027 in Tier 1; only 14.3% cross in Tier 3 (New York) by 2030.
[出典: arXiv 2604.00186 (Gupta & Kumar, 2026)]2026年3月: ATE framework identifies judges among highest-risk occupations (ATE 0.43-0.47) for agentic AI displacement — AI systems increasingly capable of end-to-end legal reasoning workflows.
[出典: Gupta & Kumar (2026) Agentic AI and Occupational Displacement]These summaries were written by AI Changing Work from the source linked with each one, and any figures in them are given as AI Changing Work summarised them; they can differ from the source's own wording and from figures shown elsewhere on this page, so check the source before relying on them. AI Changing Work matched this page's occupation to an O*NET occupation and chose, by its own judgment, case by case, which summaries relate to that O*NET occupation; a summary appearing here does not mean that its source names this occupation.