AI Vocabulary · 01 The Model

What is Data Labelling?

People tagging, writing and rating data so models can learn from it. The hidden human work behind AI.

Basic

Data labelling, also called annotation, is the human work that turns raw data into training material. People draw boxes around objects in images, write model answers, compare two responses and pick the better one, or moderate disturbing content. Much of this feeds post-training, where models learn to be helpful.

The work is global and unevenly paid. Reported rates range from cents per task for workers in Venezuela and Colombia and about $1.32 to $2 an hour in Kenya (2023) to $30 to $50 an hour for expert raters. Market researchers put the market at $3.8 billion in 2024; worker groups in Kenya and elsewhere are organising for fair pay and mental health support.

Why it matters for you

If you buy labelled data or use a labelling service, its working conditions are part of your supply chain. Rushed or underpaid labellers also produce worse labels.

Own it

Ask data vendors who does the work, where, at what pay and with what support for harmful content. For your own labels, write clear guidelines and check a sample for consistency.

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