# Police Identification and Records Officers

> Collect evidence at crime scene, classify and identify fingerprints, and photograph evidence for use in criminal and civil cases.

- **SOC code:** 33-3021.02
- **Canonical URL:** https://singulariki.com/roles/role-33-3021-02
- **Also known as:** Crime Scene Investigator (CSI), Crime Scene Technician, Criminalist, Forensic Specialist, Crime Lab Analyst (Crime Laboratory Analyst), Evidence Technician (Evidence Tech), Field Identification Specialist, Identification Technician (Identification Tech)
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Photograph crime or accident scenes for evidence records.
- Maintain records of evidence and write and review reports.
- Submit evidence to supervisors, crime labs, or court officials for legal proceedings.
- Testify in court and present evidence.
- Look for trace evidence, such as fingerprints, hairs, fibers, or shoe impressions, using alternative light sources when necessary.
- Identify, compare, classify, and file fingerprints, using systems such as Automated Fingerprint Identification System (AFIS) or the Henry Classification System.
- Dust selected areas of crime scene and lift latent fingerprints, adhering to proper preservation procedures.
- Analyze and process evidence at crime scenes, during autopsies, or in the laboratory, wearing protective equipment and using powders and chemicals.
- Package, store and retrieve evidence.
- Process film and prints from crime or accident scenes.
- Take fingerprints.
- Perform emergency work during off-hours.

**Emerging tasks** (O*NET):
- Use drone technology for aerial photography and videography of crime scenes.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Law and Government _(knowledge)_
- Active Listening _(essential_skill)_
- Speaking _(essential_skill)_
- Oral Comprehension _(ability)_
- Written Comprehension _(ability)_
- Oral Expression _(ability)_
- Deductive Reasoning _(ability)_
- Inductive Reasoning _(ability)_
- Information Ordering _(ability)_
- Near Vision _(ability)_
- Administrative _(knowledge)_
- English Language _(knowledge)_

**Skills in demand:**
- Information Ordering _(Specialized Skill)_
- Inductive Reasoning _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- Active Listening _(Common Skill)_
- English Language _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Writing _(Common Skill)_
- Time Management _(Common Skill)_
- Social Perceptiveness _(Common Skill)_
- Microsoft Word _(Common Skill)_

**Tools & technology:**
- Adobe Photoshop _(hot technology)_
- Linux _(hot technology)_
- Microsoft Access _(hot technology)_
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft PowerPoint _(hot technology)_
- Microsoft Visio _(hot technology)_
- Microsoft Windows _(hot technology)_
- Microsoft Word _(hot technology)_
- Computer aided composite drawing software
- Database software
- DataWorks Plus Digital CrimeScene

## AI exposure & outlook

- **AI task-overlap index:** 51st percentile (Moderate) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 54th percentile (Moderate) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 50th percentile (Moderate) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 48th percentile (Moderate) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 40th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** -0.7% growth (Declining); 7.8k annual openings; 117.9k → 117.1k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $93,580; 110,790 employed.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Anthropic Economic Index** (v4 (2026-01-15) + v2 (2025-03-27)) — Anthropic. https://www.anthropic.com/economic-index
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-33-3021-02_