Deep-sea Fishery Workers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 9 task statements that define Deep-sea Fishery Workers (ISCO-08 6223) score an average of 0.18 on a 0–1 exposure scale — more exposed than about 25% 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 9 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Recording fishing progress and activities, as well as weather and sea conditions, on the ship's log;”

Scores 0.33 on the 2025 scale. The task of recording fishing progress and activities, as well as weather and sea conditions, on a ship's log involves structured data entry, documentation, and elements of real-time situational awareness and manual data collection. Generative AI can assist significantly by automating parts of the documentation process, providing templates, or offering predictive inputs based on standardized weather data. However, the need for human oversight and situational judgment in dynamic environments like ships still limits full automation. Semantically similar tasks, such as "Taking readings from measuring equipment and keeping a log book of device operation reports" and "Maintaining records in the boiler operation logbook," have adjusted scores around 0.335, indicating a moderate potential for automation in tasks involving structured logging and record maintenance. Given Poland's high technological infrastructure, these efficiencies can be realized more readily, yet the need for accurate real-time reporting and context-specific inputs suggests a balanced automation score. Therefore, 0.3 reflects the potential for AI to streamline documentation and assist with data processing while acknowledging the necessity of human intervention for accurate and context-sensitive data entry.

Moving fastest, 2023 → 2025

“Directing fishing operations and supervising crew activities;”

Model capability on this task changed by +0.10 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 6223, 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.

Deep-sea Fishery Workers sit at the 25th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Deep-sea Fishery Workers rank in the 25th 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: "Recording fishing progress and activities, as well as weather and sea conditions, on the ship's log;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Deep-sea Fishery Workers". https://singulariki.com/gradient/6223-deep-sea-fishery-workers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.