Will AI replace Police Identification and Records Officers?
Estimated risk score: 37 out of 100
Lower replacement risk
Estimate from published task research, not reviewed by a person · how we estimate
Within about 19 points for 4 in 5 of the jobs we checked (18 to 56).
For the occupation as a whole, from ratings of its 15 O*NET tasks published in 2023 and calibrated to our hand scores. A specialty inside it can sit higher or lower.
O*NET-SOC 33-3021.02 · Protective Service
Tasks rated in 2023 as ones AI could do or speed up
Tasks a GPT-4-class model could do, or do in half the time or less: 3 of its 15 rated tasks. In O*NET’s words.
- Maintain records of evidence and write and review reports.
- Identify, compare, classify, and file fingerprints, using systems such as Automated Fingerprint Identification System (AFIS) or the Henry Classification System.
- Process film and prints from crime or accident scenes.
Tasks rated in 2023 as not sped up by AI
Tasks such a model was rated as not speeding up or taking over: 6 of its 15 rated tasks; the first 5 are shown. In O*NET’s words.
- Dust selected areas of crime scene and lift latent fingerprints, adhering to proper preservation procedures.
- Coordinate or conduct instructional classes or in-services, such as citizen police academy classes and crime scene training for other officers.
- Package, store and retrieve evidence.
- Testify in court and present evidence.
- Perform emergency work during off-hours.
What the job is
Collect evidence at crime scene, classify and identify fingerprints, and photograph evidence for use in criminal and civil cases.
Also called: Crime Scene Technician, Accident Examiner, Forensic Sergeant, Evidence Custodian, Criminal Specialist, Identification Tech, Crime Victim Specialist, Police Crime Scene Tech.
Source. This site includes information from the O*NET 30.1 Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license. O*NET® is a trademark of USDOL/ETA. USDOL/ETA has not approved, endorsed, or tested these modifications. (O*NET, CC BY 4.0.) Task ratings: Eloundou, Manning, Mishkin and Rock (2023), “GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models”, data used under the MIT licence (licence texts). Our change: we averaged the ratings over this occupation’s tasks and calibrated them to our 0–100 scale. It is not a prediction about any one person and not career advice. Disagree? Write to hello@yourstopwashit.com. How we estimate