An AI job boom? Here’s what the tedious, temporary work in data labelling is actually like

Elise Racine , CC BY Amid all the talk about artificial intelligence (AI) both creating and destroying jobs, a troubling reality flies under the radar.

The tasks machines can’t perform well are often offloaded onto marginalised global workers who are struggling in precarious labour markets.

They do ostensibly “automated” work under exploitative conditions .

Data work is an essential part of building and refining AI systems.

Before AI models can “learn” anything, human data workers must categorise, label, test and moderate vast volumes of text, images, audio and video, to make the data usable for AI training.

This labour is performed by an expanding global digital workforce that prepares the datasets not only for big tech, but also high-stakes industries such as banking, insurance, healthcare and government agencies, including defence.

To understand the AI workforce, I have been interviewing workers in China and Australia who prepare datasets for AI models.

The fieldwork is ongoing, but here’s what they’ve revealed so far.

Inequality is baked in My interviews with ten people to date show that precarious labour markets and marginalised social status have pushed digitally literate young workers into the data labelling industry.

As one interviewee said: We do the manual work so that they get the credit for the intelligence.

There’s a lot of inequality across the data labour market, shaped by people’s qualifications and geographic location.

Those with PhD-level or equivalent qualifications and STEM certifications can typically get more specialised tasks.

If based in the Global North , such workers tend to be higher-paid, earning A$400–800 per hour depending on the task.

But such specialised and high-paid tasks are rare and difficult to get.