Police Officers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 4 task statements that define Police Officers (ISCO-08 5412) score an average of 0.14 on a 0–1 exposure scale — more exposed than about 13% of the 427 placed occupations. Roughly 0% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Not exposed band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 4 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Directing traffic and assuming authority in the event of accidents;”

Scores 0.19 on the 2025 scale. The task of directing traffic and assuming authority in the event of accidents requires real-time decision-making, situational awareness, and human judgment to manage dynamic and potentially hazardous scenarios effectively. Generative AI can assist with data processing, suggesting traffic flow patterns, and providing communication support, but cannot replace the human element needed for immediate, context-sensitive responses and physical presence at the scene. This task is semantically similar to supervising children crossing roads (score: 0.225) and controlling vehicle movements at a protected facility (score: 0.26), both of which involve direct supervision, safety oversight, and real-time judgment. Although AI can provide useful tools for data analysis and situation monitoring, the necessity for human interaction and quick adaptation limits full automation. Given the high-income and technological context of Poland, the score reflects a balance between AI's supportive capabilities and the substantial human involvement required, justifying a score of 0.23.

Moving fastest, 2023 → 2025

“Directing traffic and assuming authority in the event of accidents;”

Model capability on this task changed by +0.09 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 5412, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

• Police and Sheriff's Patrol Officers
• Customs and Border Protection Officers
• First-Line Supervisors of Police and Detectives
• Transit and Railroad Police

In context

Part of the 5 - Service and sales workers major group. Return to the full gradient to see how the whole group sits.

Police Officers sit at the 13th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Police Officers rank in the 13th percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 0% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure fell by 0.01 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Directing traffic and assuming authority in the event of accidents;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Police Officers". https://singulariki.com/gradient/5412-police-officers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.