Inland and Coastal Waters Fishery Workers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 10 task statements that define Inland and Coastal Waters Fishery Workers (ISCO-08 6222) score an average of 0.17 on a 0–1 exposure scale — more exposed than about 24% 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 10 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Keeping records of transactions, fishing activities, weather and sea conditions and estimating costs and budgets;”

Scores 0.42 on the 2025 scale. The task of "Keeping records of transactions, fishing activities, weather and sea conditions, and estimating costs and budgets" involves a mix of structured documentation, data analysis, and contextual judgment. Generative AI is quite capable of assisting with generating reports, structuring documentation, and performing basic data analysis, especially for tasks involving repetitive and structured data processing. Based on contextually similar tasks, "Maintaining necessary breeding and economic documentation" had a score of approximately 0.275 to 0.295, indicating some potential for automation, although these tasks also require substantial human oversight for accuracy and context. The task of "Organizing and supervising catches and fishing" with a score of 0.25 reflected a lower automation potential due to the necessity of physical involvement and real-time decision-making. Given that this task is in a high-access technological environment like Poland and allows for some level of document automation and data analysis, the adjusted score is set at 0.4. This score reflects the capabilities of AI to support automation in documentation and estimation tasks while acknowledging the need for human oversight due to the task's variability and contextual nature, particularly in assessing real-time fishing activities and environmental conditions.

Moving fastest, 2023 → 2025

“Directing fishing operations, and supervising fishing crew members.”

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 6222, 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.

• First-Line Supervisors of Farming, Fishing, and Forestry Workers

In context

Part of the 6 - Skilled agricultural, forestry and fishery workers major group. Return to the full gradient to see how the whole group sits.

Inland and Coastal Waters Fishery Workers sit at the 24th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Inland and Coastal Waters Fishery Workers rank in the 24th 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 0.06 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Keeping records of transactions, fishing activities, weather and sea conditions and estimating costs and budgets;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Inland and Coastal Waters Fishery Workers". https://singulariki.com/gradient/6222-inland-and-coastal-waters-fishery-workers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.