Garbage and Recycling Collectors

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 4 task statements that define Garbage and Recycling Collectors (ISCO-08 9611) score an average of 0.09 on a 0–1 exposure scale — more exposed than about 1% 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

“Riding on or in garbage and recycling trucks;”

Scores 0.11 on the 2025 scale. The task of riding on or in garbage and recycling trucks involves significant physical activity and situational awareness, similar to other tasks requiring manual dexterity and environmental interaction, such as emptying trash cans, sweeping streets, and operating self-propelled street cleaning equipment. These tasks have low potential for automation using current Generative AI technology, which lacks the capacity to physically execute tasks or make real-time adjustments required by dynamic, on-the-ground operations. Scores for semantically similar tasks were generally low, reflecting minimal automation potential. Despite the advanced technological infrastructure in a country like Poland, the core elements of this task remain firmly manual and require physical presence and human judgment, limiting the role AI can play. Therefore, a score of 0.125 adequately reflects the minor but potential indirect AI contributions, such as scheduling or route optimization, without implying direct automation of the core task.

Moving fastest, 2023 → 2025

“Riding on or in garbage and recycling trucks;”

Model capability on this task changed by +0.01 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 9611, 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.

• Refuse and Recyclable Material Collectors

In context

Part of the 9 - Elementary occupations major group. Return to the full gradient to see how the whole group sits.

Garbage and Recycling Collectors sit at the 1st percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Garbage and Recycling Collectors rank in the 1st 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: "Riding on or in garbage and recycling trucks;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Garbage and Recycling Collectors". https://singulariki.com/gradient/9611-garbage-and-recycling-collectors.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.