Refuse Sorters

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 6 task statements that define Refuse Sorters (ISCO-08 9612) score an average of 0.18 on a 0–1 exposure scale — more exposed than about 27% 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 6 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Selling recyclable or reusable materials.”

Scores 0.41 on the 2025 scale. The task of selling recyclable or reusable materials involves understanding market trends, engaging potential buyers, and managing transactions, which have components amenable to Generative AI automation. AI can assist in creating targeted marketing campaigns, analyzing data for market trends, and suggesting pricing strategies. Similar tasks like "Promoting manufactured eco-friendly products" (adjusted score 0.385) and "Organizing the sale of live animals" (adjusted score 0.37) highlight the role AI can play in data-driven and communicative aspects of sales, although human interaction and negotiation remain critical for closing deals. In high-income regions like Poland, the infrastructure supports the integration of AI into sales processes, enhancing the potential for automation. Given these parallels and AI’s limitations in fully automating personalized buyer interactions and strategic decision-making, an adjusted score of 0.41 reflects the potential of AI to enhance, but not completely automate, the task of selling recyclable or reusable materials.

Moving fastest, 2023 → 2025

“Selling recyclable or reusable materials.”

Model capability on this task changed by +0.21 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 9612, 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.

Refuse Sorters sit at the 27th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Refuse Sorters rank in the 27th 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 rose by 0.04 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Selling recyclable or reusable materials.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Refuse Sorters". https://singulariki.com/gradient/9612-refuse-sorters.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.