Global Inequalities in the Production of Artificial Intelligence: A Four-Country Study on Data Work

TL;DR

This study examines global inequalities in AI data work by comparing conditions and profiles of workers in Venezuela, Brazil, Madagascar, and France, revealing colonial-like dependencies and economic disparities in AI production.

Contribution

It provides a comparative analysis of data workers across four countries, highlighting the colonial-like supply chains and inequalities in AI data labor markets.

Findings

Data work in AI is linked to colonial-like supply chains.

Lower-income countries face more informal and less regulated labor conditions.

Inequalities in AI data work mirror historical economic dependencies.

Abstract

Labor plays a major, albeit largely unrecognized role in the development of artificial intelligence. Machine learning algorithms are predicated on data-intensive processes that rely on humans to execute repetitive and difficult-to-automate, but no less essential, tasks such as labeling images, sorting items in lists, recording voice samples, and transcribing audio files. Online platforms and networks of subcontractors recruit data workers to execute such tasks in the shadow of AI production, often in lower-income countries with long-standing traditions of informality and lessregulated labor markets. This study unveils the resulting complexities by comparing the working conditions and the profiles of data workers in Venezuela, Brazil, Madagascar, and as an example of a richer country, France. By leveraging original data collected over the years 2018-2023 via a mixed-method design, we highlight how the cross-country supply chains that link data workers to core AI production sites are reminiscent of colonial relationships, maintain historical economic dependencies, and generate inequalities that compound with those inherited from the past. The results also point to the importance of less-researched, non-English speaking countries to understand key features of the production of AI solutions at planetary scale.