Private Detectives and Investigators
Protective Service Occupations- O*NET-SOC-Code
- 33-9021.00
Gather, analyze, compile, and report information regarding individuals or organizations to clients, or detect occurrences of unlawful acts or infractions of rules in private establishment.
Berufsbezeichnungen und Aufgabenbeschreibungen werden wie veröffentlicht auf Englisch angezeigt. Beschriftungen, einschließlich der Aufgabenarten, sind übersetzt.
KI-Exposition
- Datenquelle: BLSVeröffentlicht: 2026-08
Hoch· relativ
NiedrigVier relative BänderSehr hochWert auf Berufsgruppenebene
Skala, Bezugsmenge und Quelle
Vier relative Bänder (Niedrig / Mittel / Hoch / Sehr hoch)
831 detaillierte Berufe in der BLS-Beschäftigungsprojektionstabelle. Der Wert wird je Code der National Employment Matrix (NEM) vergeben, daher erhalten Berufe mit demselben NEM-Code dasselbe Band
- Datenquelle: AnthropicVeröffentlicht: 2026-03
0.000
0.000Spannweite der hier geführten Werte0.745Skala, Bezugsmenge und Quelle
Index der beobachteten Exposition, 0–1 wie veröffentlicht
Auf O*NET-Aufgaben abgebildet
Veröffentlicht je SOC-2018-Beruf; jeder O*NET-Beruf mit demselben SOC-2018-Code erhält diesen Wert
- Datenquelle: ILOVeröffentlicht: 2025
0.39
0.09Spannweite der hier geführten Werte0.70Wert auf Berufsgruppenebene
Skala, Bezugsmenge und Quelle
Index der Exposition gegenüber generativer KI, 0–1 wie veröffentlicht
Veröffentlicht je ISCO-08-Berufsgattung. Mit diesem Beruf ganz oder teilweise verknüpft durch Anwendung der Umsteigeschlüssel des U.S. Bureau of Labor Statistics (ISCO-08 zu SOC 2010, SOC 2010 zu SOC 2018) in veröffentlichter Form
Welche Art von Wert diese Quelle veröffentlicht
Die Kategorie von BLS ist ein relativer Rang und kein absolutes Niveau, und sie ist keine eigene Messung: Sie fasst die Perzentilränge des Berufs aus mehreren veröffentlichten Studien in vier Bänder zusammen. Sie ist weder eine Beschäftigungs- oder Lohnprognose noch eine Einführungswahrscheinlichkeit und unterscheidet nicht zwischen Automatisierung und Augmentierung.
KI-Exposition (OpenAI-Rubrik)
17 bewertete Aufgaben · 11 Aufgaben mit β ≥ 0,5 (64.7%)
β = direkte Exposition (E1) + 0,5 × Exposition bei verfügbaren Werkzeugen (E2), gemäß der Definition des Quell-Repositorys.
- Quelleinheit: Aufgaben aus O*NET 27.2 → Berufscode aus O*NET 31.0
- 3 bewertete Aufgaben stehen nicht in der Aufgabenliste von O*NET 31.0; ihre Werte bleiben wie für O*NET 27.2 veröffentlicht.
- Quelle
- OpenAI "GPTs are GPTs" exposure rubric
- Ausgabe
- gh-main-0471612
- Lizenz
- MIT License, Copyright (c) 2024 OpenAI
Aufgaben
Aufgabenbeschreibungen aus der O*NET® 31.0 Database, Kernaufgaben zuerst.
| Aufgabe | Art | β (OpenAI) |
|---|---|---|
| Write reports or case summaries to document investigations. | Kern | 1 |
| Search computer databases, credit reports, public records, tax or legal filings, or other resources to locate persons or to compile information for investigations. | Kern | 0,5 |
| Obtain and analyze information on suspects, crimes, or disturbances to solve cases, to identify criminal activity, or to gather information for court cases. | Kern | 0,5 |
| Conduct private investigations on a paid basis. | Kern | 0,5 |
| Testify at hearings or court trials to present evidence. | Kern | 0 |
| Question persons to obtain evidence for cases of divorce, child custody, or missing persons or information about individuals' character or financial status. | Kern | 0,5 |
| Observe and document activities of individuals to detect unlawful acts or to obtain evidence for cases, using binoculars and still or video cameras. | Kern | 0,5 |
| Expose fraudulent insurance claims or stolen funds. | Kern | 0,5 |
| Confer with establishment officials, security departments, police, or postal officials to identify problems, provide information, or receive instructions. | Kern | 0 |
| Conduct personal background investigations, such as pre-employment checks, to obtain information about an individual's character, financial status, or personal history. | Kern | 0,5 |
| Alert appropriate personnel to suspects' locations. | Ergänzend | 0,5 |
| Perform undercover operations, such as evaluating the performance or honesty of employees by posing as customers or employees. | Ergänzend | 0 |
| Investigate companies' financial standings, or locate funds stolen by embezzlers, using accounting skills. | Ergänzend | 0,5 |
| Count cash and review transactions, sales checks, or register tapes to verify amounts or to identify shortages. | Ergänzend | 0,5 |
Berufsinformationen
Quellen und Namensnennung
This page includes information from the O*NET® 31.0 Database (https://www.onetcenter.org/database.html) by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/). O*NET® is a trademark of USDOL/ETA. AI Changing Work has modified all or some of this information: the O*NET-SOC code, title and task statements are reproduced in English without change; task-type labels are shown in the page's language and tasks are listed core first; any Korean occupation title shown on the Korean-language page is AI Changing Work's translation; any KSCO-8 unit groups linked to this occupation were paired with it by AI Changing Work's judgment, and the relation labels and statuses are AI Changing Work's additions. USDOL/ETA has not approved, endorsed, or tested these modifications.
Any AI exposure figures on this page are published by third parties, not by AI Changing Work, and none is part of the O*NET information. OpenAI publishes task-level scores (MIT License) for O*NET 27.2 task statements; each is shown next to the O*NET 31.0 task statement with the same task ID, whose wording can differ from the 27.2 statement that was scored. OpenAI also publishes occupation-level scores for O*NET-SOC codes in the same release, and any such score is shown on the O*NET occupation with the same code. Anthropic publishes an observed exposure index in the Anthropic Economic Index (CC-BY), and the U.S. Bureau of Labor Statistics publishes relative AI exposure categories (public domain); both are published per SOC code, and each value is shown on every O*NET occupation with that code. The International Labour Organization publishes a generative AI exposure index in ILO Working Paper 140 (CC BY 4.0) for ISCO-08 unit groups; AI Changing Work links those groups to O*NET occupations by applying the U.S. Bureau of Labor Statistics ISCO-08 to 2010 SOC and 2010 SOC to 2018 SOC crosswalks as published, without case-by-case selection, and these crosswalks match many groups only in part. Where several unit groups are linked, each group's published value is listed, and any summary shows only the lowest and highest of those values with the number of groups; no exposure figure is averaged or recalculated. Any employment figures are published by the U.S. Bureau of Labor Statistics for the SOC group containing this occupation. Each source is credited where its figures are shown.
O*NET OnLine: 33-9021.00 Private Detectives and Investigators
KSCO 코드·명칭: 한국표준직업분류(제8차 개정) — 통계청 고시 제2024-328호 (2024-07-01 고시, 2025-01-01 시행). 저작권법 제7조 제2호의 고시 항목이다. 명칭 표기(가운뎃점·띄어쓰기)는 해설서 2차 정오 반영판의 표기를 따랐으며, 고시 항목표와는 18개 명칭에서 가운뎃점 글리프나 띄어쓰기만 다르다. 통계청은 2025년 10월 국가데이터처로 개편되었다. 이 페이지의 KSCO 연결은 통계청·국가데이터처의 공식 연계표가 아니다.