Chapter 09 · Sustainability & AISustainability & AI
Data colonialism
Definition
A critical concept describing the extraction of data from people and territories — concentrated in the hands of a few powerful actors — as a continuation of historical colonial resource appropriation.
References
This source provides part of the technical or institutional basis for the definition.
This source supports the explanation of how the term is applied, measured or governed in practice.
Overview
What it means
Coined by Couldry and Mejias (2019), the term frames large-scale data collection as appropriation of human life as raw material, disproportionately affecting lower-income countries whose data and labour feed AI systems while value accrues elsewhere. It connects AI supply chains — from labelling workforces to scraped content — to global inequality.
How it is used
The concept informs debates on data governance, benefit-sharing and digital sovereignty, and critiques of AI development models that extract data and labour from the Global South. It appears in UNESCO-era AI ethics discourse and data-justice advocacy.
Why it matters
Data colonialism gives language to the distributional question inside AI: who provides the raw material, who does the invisible work, and who captures the value. Sustainability's equity pillar applies to the digital economy too.