Forensic Science Technicians
Life, Physical, and Social Science Occupations- O*NET-SOC code
- 19-4092.00
Collect, identify, classify, and analyze physical evidence related to criminal investigations. Perform tests on weapons or substances, such as fiber, hair, and tissue to determine significance to investigation. May testify as expert witnesses on evidence or crime laboratory techniques. May serve as specialists in area of expertise, such as ballistics, fingerprinting, handwriting, or biochemistry.
AI exposure
- 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
0.000
0.000Range of values carried here0.745Scale, 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
0.26
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.
AI exposure (OpenAI rubric)
21 rated tasks · 16 tasks with β ≥ 0.5 (76.2%)
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
- 1 rated task is not in the O*NET 31.0 task list; its score is kept as published for O*NET 27.2.
- Source
- OpenAI "GPTs are GPTs" exposure rubric
- Release
- gh-main-0471612
- License
- MIT License, Copyright (c) 2024 OpenAI
Tasks
Task statements from the O*NET® 31.0 Database, core tasks first.
| Task | Type | β (OpenAI) |
|---|---|---|
| Testify in court about investigative or analytical methods or findings. | Core | 0.5 |
| Operate and maintain laboratory equipment and apparatus. | Core | 0 |
| Prepare solutions, reagents, or sample formulations needed for laboratory work. | Core | 0 |
| Collect evidence from crime scenes, storing it in conditions that preserve its integrity. | Core | 0 |
| Collect impressions of dust from surfaces to obtain and identify fingerprints. | Core | 0.5 |
| Analyze gunshot residue and bullet paths to determine how shootings occurred. | Core | 0.5 |
| Visit morgues, examine scenes of crimes, or contact other sources to obtain evidence or information to be used in investigations. | Core | 0.5 |
| Confer with ballistics, fingerprinting, handwriting, documents, electronics, medical, chemical, or metallurgical experts concerning evidence and its interpretation. | Core | 0.5 |
| Keep records and prepare reports detailing findings, investigative methods, and laboratory techniques. | Core | 1 |
| Use chemicals or other substances to examine latent fingerprint evidence and compare developed prints to those of known persons in databases. | Core | 0.5 |
| Train new technicians or other personnel on forensic science techniques. | Core | 0 |
| Examine and analyze blood stain patterns at crime scenes. | Core | 0.5 |
| Use photographic or video equipment to document evidence or crime scenes. | Core | 0.5 |
| Measure and sketch crime scenes to document evidence. | Core | not rated |
| Examine footwear, tire tracks, or other types of impressions. | Core | not rated |
| Interpret laboratory findings or test results to identify and classify substances, materials, or other evidence collected at crime scenes. | Supplemental | 0.5 |
| Identify and quantify drugs or poisons found in biological fluids or tissues, in foods, or at crime scenes. | Supplemental | 0 |
| Reconstruct crime scenes to determine relationships among pieces of evidence. | Supplemental | 0.5 |
| Examine firearms to determine mechanical condition and legal status, performing restoration work on damaged firearms to obtain information, such as serial numbers. | Supplemental | 0.5 |
| Compare objects, such as tools, with impression marks to determine whether a specific object is responsible for a specific mark. | Supplemental | 0.5 |
| Review forensic analysts' reports for technical merit. | Supplemental | 0.5 |
| Determine types of bullets and specific weapons used in shootings. | Supplemental | 0.5 |
| Examine physical evidence, such as hair, biological fluids, fiber, wood, or soil residues to obtain information about its source and composition. | Supplemental | not rated |
| Analyze data from computers or other digital media sources for evidence related to criminal activity. | Supplemental | not rated |
Occupation information
Sources and attribution
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: 19-4092.00 Forensic Science Technicians
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.