Police Identification and Records Officers
Protective Service Occupations- O*NET-SOC code
- 33-3021.02
Collect evidence at crime scene, classify and identify fingerprints, and photograph evidence for use in criminal and civil cases.
- Data source: BLSPublished: 2026-08
High· relative
LowFour relative bandsVery highGroup-level value
Scale, basis and source
Four relative bands (Low / Moderate / High / Very high)
831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band
- Data source: AnthropicPublished: 2026-03
0.037
0.000Range of values carried here0.745Group-level value
Scale, basis and source
Observed exposure index, 0–1 as published
Mapped onto O*NET tasks
Published per SOC 2018 occupation; every O*NET occupation with the same SOC 2018 code carries this value
- Data source: ILOPublished: 2025
0.23
0.09Range of values carried here0.70Group-level value
Scale, basis and source
Generative AI exposure index, 0–1 as published
Published per ISCO-08 unit group. Linked to this occupation, wholly or in part, by applying U.S. Bureau of Labor Statistics crosswalks (ISCO-08 to 2010 SOC, 2010 SOC to 2018 SOC) as published
What kind of figure this source publishes
The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.
Task-level exposure
Show 13 hidden tasks15 rated tasks · 9 tasks with β ≥ 0.5 (60.0%)
β = direct exposure (E1) + 0.5 × exposure when tools are available (E2), per the source repository's definition.
- Source unit: O*NET 27.2 tasks → O*NET 31.0 occupation code
- All rated tasks are in the O*NET 31.0 task list.
- Source
- OpenAI "GPTs are GPTs" exposure rubric
- Release
- gh-main-0471612
- License
- MIT License, Copyright (c) 2024 OpenAI
| Task (O*NET 20.1) | Claude.aiRaw / share % |
|---|---|
Coordinate or conduct instructional classes or in-services, such as citizen police academy classes and crime scene training for other officers.33-3021.02 | 0.0025100.0 |
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 (O*NET 27.2) | βE1 + 0.5 × E2 |
|---|---|
Maintain records of evidence and write and review reports.O*NET Task ID 18708 | 1.0 |
Photograph crime or accident scenes for evidence records.O*NET Task ID 2120 | 0.5 |
Look for trace evidence, such as fingerprints, hairs, fibers, or shoe impressions, using alternative light sources when necessary.O*NET Task ID 2123 | 0.5 |
Process film and prints from crime or accident scenes.O*NET Task ID 2129 | 0.5 |
Submit evidence to supervisors, crime labs, or court officials for legal proceedings.O*NET Task ID 18709 | 0.5 |
Identify, compare, classify, and file fingerprints, using systems such as Automated Fingerprint Identification System (AFIS) or the Henry Classification System.O*NET Task ID 18710 | 0.5 |
Serve as technical advisor and coordinate with other law enforcement workers or legal personnel to exchange information on crime scene collection activities.O*NET Task ID 18711 | 0.5 |
Analyze and process evidence at crime scenes, during autopsies, or in the laboratory, wearing protective equipment and using powders and chemicals.O*NET Task ID 21036 | 0.5 |
Create sketches and diagrams, by hand or computer software, to depict crime scenes.O*NET Task ID 21037 | 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
All O*NET 31.0 task statements (16)
All O*NET 31.0 task statements for this occupation (English, as published; task type core or supplemental). Exposure values are not listed here; they are in the table above, next to each task statement as worded in the O*NET version each source used.
| Task (O*NET 31.0) | Type |
|---|---|
| Photograph crime or accident scenes for evidence records. | Core |
| Testify in court and present evidence. | Core |
| Dust selected areas of crime scene and lift latent fingerprints, adhering to proper preservation procedures. | Core |
| Look for trace evidence, such as fingerprints, hairs, fibers, or shoe impressions, using alternative light sources when necessary. | Core |
| Package, store and retrieve evidence. | Core |
| Perform emergency work during off-hours. | Core |
| Process film and prints from crime or accident scenes. | Core |
| Maintain records of evidence and write and review reports. | Core |
| Submit evidence to supervisors, crime labs, or court officials for legal proceedings. | Core |
| Serve as technical advisor and coordinate with other law enforcement workers or legal personnel to exchange information on crime scene collection activities. | Core |
| Coordinate or conduct instructional classes or in-services, such as citizen police academy classes and crime scene training for other officers. | Core |
| Analyze and process evidence at crime scenes, during autopsies, or in the laboratory, wearing protective equipment and using powders and chemicals. | Core |
| Create sketches and diagrams, by hand or computer software, to depict crime scenes. | Core |
| Take fingerprints. | Core |
| Identify, compare, classify, and file fingerprints, using systems such as Automated Fingerprint Identification System (AFIS) or the Henry Classification System. | Supplemental |
| Interview survivors, witnesses, suspects, and other law enforcement personnel. | Supplemental |
0.13/ 3· Robot exposure index
Scale, basis and source
Each physical task is graded 0–3 by the setting in which robots that have been deployed, sold or demonstrated could do it (0 none · 1 purpose-built robot settings such as a fenced machine cell or a bottling line · 2 structured facilities such as a hospital pharmacy or a container port · 3 unstructured settings such as a private home or a construction site), and the grades are averaged by share of working time
Physical tasks among this occupation's O*NET 29.3 tasks
Anthropic estimated the grades and time shares with Claude Opus 5 using web search, as of 2026.
The robot exposure index is a different measure from the AI exposure figures on this site and cannot be added to or compared with them. It does not measure actual adoption or cost, and 0 also covers occupations with no physical tasks.
Tier of each physical task
Robot environment tiers (E0–E3)
E0 noneDetails
Rated not doable by robots that have been deployed, sold or demonstrated, even in a purpose-built setting
E1 purpose-built robot settingsDetails
Rated doable in a setting built around the machine, such as a fenced machine cell or a bottling line
E2 structured facilitiesDetails
Rated doable in a human work facility that an institution keeps to a working standard, such as a hospital pharmacy or a container port
E3 unstructured settingsDetails
Rated doable in a setting that no institution keeps to a working standard, such as a private home or a construction site
Only tasks classified as physical are listed; other tasks have no tier in the source.
| Task (O*NET 29.3) | Robot environment tier |
|---|---|
| Photograph crime or accident scenes for evidence records. | E0 none |
| Dust selected areas of crime scene and lift latent fingerprints, adhering to proper preservation procedures. | E0 none |
| Look for trace evidence, such as fingerprints, hairs, fibers, or shoe impressions, using alternative light sources when necessary. | E0 none |
| Package, store and retrieve evidence. | E1 purpose-built robot settings |
| Submit evidence to supervisors, crime labs, or court officials for legal proceedings. | E1 purpose-built robot settings |
| Analyze and process evidence at crime scenes, during autopsies, or in the laboratory, wearing protective equipment and using powders and chemicals. | E0 none |
| Perform emergency work during off-hours. | E0 none |
Occupation information
Employment outlook (BLS)
Values for the whole SOC 33-3021 group (Detectives and criminal investigators), not for this O*NET occupation alone.
- Employment, 2025
- 121K
- Projected employment, 2035
- 121K
- Change (2025–35)
- +0.2%
- Median annual wage, 2025
- $93,790
- Typical education needed for entry
- High school diploma or equivalent
Korea classification links (KSCO)
Links from the current release of the Korea Occupation Direct Crosswalk v1.1, made by AI Changing Work. Relation values are read from the O*NET-SOC occupation side.
| KSCO code | KSCO title | Relation | Status |
|---|---|---|---|
| 41112 | 일반 경찰관 및 수사관 | This occupation is narrower | confirmed |
Relation and status definitions(kr-direct v1.1)
ILO occupational description
AI Changing Work links the ISCO-08 unit groups below to this occupation by applying the U.S. Bureau of Labor Statistics crosswalks (2018 SOC to 2010 SOC, 2010 SOC to ISCO-08) as published, without case-by-case selection; each link may be whole or partial.
- ISCO-08 3355Police Inspectors and Detectives
The definition, tasks and examples below are the ILO's description of that unit group, not of this occupation.
Police inspectors and detectives investigate facts and circumstances relating to crimes committed in order to identify suspected offenders and obtain information not readily available or apparent concerning establishments or the circumstances and behaviour of persons, mostly in order to prevent crimes.
Tasks
- (a) establishing contacts and sources of information about crimes planned or committed, in order to prevent crimes or identify suspected offenders;
- (b) obtaining and verifying evidence by examining crime and accident scenes for clues and physical evidence, interviewing witnesses and suspects and analysing documents and computer files;
- (c) analysing evidence in order to solve crimes, identify criminal activity and gather information for court cases;
- (d) establishing contacts and sources of information not readily available or apparent concerning establishments or the circumstances and behaviour of persons, usually with the aim of preventing a crime;
- (e) making arrests;
- (f) testifying in courts of law or reporting to superiors about circumstances and results of investigations.
Example occupations
Police detective · Police inquiry agent · Police inspector
Sources: ILO ISCO-08 — details: Credits
ESCO occupational vocabulary
Each entry below is matched to this occupation in the published ESCO-O*NET Crosswalk (version 1, 2022). The match types are that crosswalk's; the wording is AI Changing Work's.
- criminal investigatorESCO 3355.1Overlapping, not the same
Criminal investigators examine and process the scenes of crimes and the evidence found in them. They handle and protect the evidence compliant with rules and regulations, and isolate the scene from outside influence. They photograph the scene, ensure the maintenance of the evidence, and write reports.
- police detectiveESCO 3355.2Overlapping, not the same
Police detectives gather and compile evidence which assists them in the solution of crimes. They use investigative techniques to gather evidence, and interview all parties connected to their line of enquiry, and cooperate with other police department divisions to gather the evidence.
Source: European Commission, DG EMPL — ESCO API 2026-08-08 (bridge v1.2.1)
License: CC BY 4.0 (EU Reuse Notice)
ESCO reuse terms and the modification notice are in Credits.
France classification links (ROME)
AI Changing Work's links between this occupation and ROME 4.0 job profiles, judged with AI models. Relations are read from this occupation's side. Links marked “provisional” were adopted on the judgments of two judges from the same model family (Claude) alone, or by an operator rule applied to their judgments, so they rest on weaker evidence than confirmed links.
| ROME code | ROME title (French) | Relation |
|---|---|---|
| K1721 | Technicien / Technicienne en investigation criminelle | Same scopeprovisional |
Source: France Travail — Répertoire Opérationnel des Métiers et des Emplois (ROME 4.0), version 61, last updated 15 June 2026 (https://www.data.gouv.fr/datasets/repertoire-operationnel-des-metiers-et-des-emplois-rome). Licence Ouverte 2.0.
ROME codes and titles are reproduced unchanged; the links to O*NET-SOC occupations are AI Changing Work's own judgment and are not a France Travail correspondence.
(AI Changing Work link release: fr-direct v1)
Spain classification links (CNO-11)
AI Changing Work's links between this occupation and CNO-11 occupations, judged with AI models. Relations are read from this occupation's side.
| CNO-11 code | CNO-11 title (Spanish) | Relation |
|---|---|---|
| 3631 | Técnicos de la policía nacional, autonómica y local | This occupation is narrowerprovisional |
| 3632 | Suboficiales de la guardia civil | Related, not a matchprovisional |
Source: Instituto Nacional de Estadística (INE), National Classification of Occupations 2011 (CNO-11, Royal Decree 1591/2010) — www.ine.es · Licensed CC BY 4.0. CNO-11 codes and Spanish titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own judgment and are not an INE correspondence.
(AI Changing Work link release: es-direct v1)
Germany classification links (KldB)
AI Changing Work's links between this occupation and KldB occupations, judged with AI models. Relations are read from this occupation's side.
| KldB code | KldB title (German) | Relation |
|---|---|---|
| 53223 | Berufe im Kriminaldienst - komplexe Spezialistentätigkeiten | This occupation is narrower |
| 53224 | Berufe im Kriminaldienst - hoch komplexe Tätigkeiten | Related, not a match |
Source: © Statistik der Bundesagentur für Arbeit — Klassifikation der Berufe 2010 – überarbeitete Fassung 2020, https://statistik.arbeitsagentur.de. KldB codes and titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own mapping (eigene Zuordnung) and are not a Bundesagentur für Arbeit correspondence.
(AI Changing Work link release: de-direct v1)
Japan classification links (JSCO)
AI Changing Work's links between this occupation and JSCO occupations, judged with AI models. Relations are read from this occupation's side.
| JSCO code | JSCO title (Japanese) | Relation |
|---|---|---|
| 441 | 警察官 | Related, not a match |
| 249 | 他に分類されない専門的職業従事者 | Related, not a matchprovisional |
Source: Ministry of Internal Affairs and Communications (MIC), Japan Standard Occupational Classification (Dec. 2009, MIC Notification No. 555). JSCO codes and Japanese titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own judgment and are not a MIC correspondence.
(AI Changing Work link release: jp-direct v1)
Sources, licenses and AI Changing Work's modification and translation notices for this page: Credits & sources