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Elementary occupations · code 9216

Fishery and aquaculture labourers

Fishery and aquaculture labourers perform simple and routine tasks to cultivate, catch and harvest fish and seafood in aquaculture and in inland, coastal and deep sea fishing operations. Tasks include - (a) cleaning the sea-bed and feeding fish and molluscs that are being cultivated; (b) gathering seaweed, sea mosses, clams and other molluscs; (c) preparing nets, lines and other fishing tackle and other deck equipment; (d) operating fishing gear to catch fish and other marine life; (e) cleaning, sorting and packing fish and seafood in ice and salt, and stowing catch in hold; (f) cleaning deck surfaces and fish hold; (g) handling mooring lines during docking. Examples of the occupations classified here: - Aquaculture labourer - Fishery labourer Some related occupations classified elsewhere: - Fish farmer - 6221 - Coastal fishery skipper - 6222 - Fisher (coastal waters) - 6222 - Fisher (inland waters) - 6222 - Deep sea fisher - 6223

None of this job's tasks scored high enough to count. It ranks 22 of 427 for holding up against AI.

Also known as

  • water-based aquaculture worker
  • aquaculture harvesting worker
  • aquaculture cage mooring worker
  • on foot aquatic resources collector

Job titles from the EU's ESCO list. Search for any of them on the jobs page and you'll end up here.

88.9
Holds up
against AI Not Exposed

AI could take on 11.1 of 100, on average
so it holds up at 100 − 11.1 = 88.9

That's the whole sum, and you can check it against the ILO's study.

69.9
How hands-on
out of 100

From O*NET, averaged across 2 US jobs

37.8
How fiddly
and cramped

finger skill 42.9 · hand skill 42.9 · cramped spaces 27.7 · averaged

Kind of hands-on work · hands-on 50+ · fiddly under 39.4 Hands-on, less fiddly work

Less fine handwork and fewer cramped spaces than a typical hands-on job. That clears one hurdle for machines. It doesn't mean machines are doing this work yet.

estimated O*NET 31.0 BLS occupational crosswalk chain ISCO-08 to 2010 SOC (Aug 2012, rev. Jun 2015); 2010 to 2018 SOC (Nov 2017); O*NET-SOC 2019 taxonomy data 0.7-work-setting measures chosen by Frey & Osborne (2017) · the grouping is a sorting, not a measurement

This number is probably too high

This score only looks at AI tools like chatbots. It doesn't look at robots at all — and this job rates 69.9 out of 100 for how hands-on it is, which is exactly where robots come in.

Have a look at the tasks below and judge for yourself. The ILO scored them low because a chatbot can't do them. Whether a machine with hands could is a different question, and we can't answer it yet.

The tasks behind the score

The score is the average of these. A job is really a bundle of tasks, and an average can hide how uneven that bundle is.

The task AI could do most of scores 15, while the average is 11.1. A big gap means some of this job is wide open to AI and some of it barely at all. That's quite different from a job where everything sits somewhere in the middle, even when the averages match.

  1. 15

    Handling mooring lines during docking.

    Very Low
  2. 13

    Cleaning the sea-bed and feeding fish and molluscs that are being cultivated

    Very Low
  3. 12.5

    Operating fishing gear to catch fish and other marine life

    Very Low
  4. 11.8

    Preparing nets, lines and other fishing tackle and other deck equipment

    Very Low
  5. 11.5

    Cleaning, sorting and packing fish and seafood in ice and salt, and stowing catch in hold

    Very Low
  6. 7.8

    Cleaning deck surfaces and fish hold

    Very Low
  7. 5.9

    Gathering seaweed, sea mosses, clams and other molluscs

    Very Low

7 tasks · the High to Very Low labels are the ILO's

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.7-work-setting

Compared with similar jobs

There are 33 jobs in the “Elementary occupations” group, averaging 84.9. This one is above that.