Mining and Quarrying Labourers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 7 task statements that define Mining and Quarrying Labourers (ISCO-08 9311) score an average of 0.11 on a 0–1 exposure scale — more exposed than about 4% 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 7 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Assisting miners and quarriers in maintaining machinery, equipment, and mine and quarry installations;”

Scores 0.14 on the 2025 scale. The task of assisting miners and quarriers in maintaining machinery, equipment, and mine and quarry installations involves mostly physical work, similar to tasks such as "Maintaining grinding equipment" (score 0.1375) and "Installing power networks in underground mining excavations" (score 0.12). These tasks require manual dexterity and real-time problem-solving, where Generative AI can only provide limited assistance, such as offering maintenance guidelines or diagnostics. The semantic similarity to tasks like "Operating mining machines and equipment" (score 0.135) highlights the reliance on human skills for physical manipulation and handling tasks that AI cannot yet automate, even in a high-income country like Poland with widespread access to digital tools. Thus, the adjusted score acknowledges AI’s potential role in supporting roles but not in replacing human intervention for the hands-on aspects of mining maintenance.

Moving fastest, 2023 → 2025

“Assembling and dismantling mining equipment;”

Model capability on this task changed by +0.02 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 9311, 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.

• Helpers--Extraction Workers
• Extraction Workers, All Other

In context

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

Mining and Quarrying Labourers sit at the 4th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Mining and Quarrying Labourers rank in the 4th 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 0.02 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Assisting miners and quarriers in maintaining machinery, equipment, and mine and quarry installations;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Mining and Quarrying Labourers". https://singulariki.com/gradient/9311-mining-and-quarrying-labourers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.