The hidden human work behind automation

Probabilistic automation systems are more than just algorithms. They also require a huge amount of human labour behind the scenes.

Before a system can learn from data, people have to prepare that data. There is work involved in annotation, labelling, transcription, moderation and many other tasks. Even after a model is trained, humans are often required to evaluate and improve its outputs.

This creates an interesting contradiction. We call these systems "automation", but there are still many humans working behind the scenes to make that automation possible.

A lot of this work is concentrated in countries across Africa and South and East Asia. In many cases, workers can face problems related to low wages, temporary contracts, lack of worker protection and limited access to healthcare and other support. Some moderation work can also involve exposure to disturbing or harmful content.

When we use an AI system, we normally only see the final product. We don't see all the people who helped create the data that made the system possible.

So the question becomes: how much of the "automated" world is actually dependent on invisible human labour?