Where does AI come from? A global case study across Europe, Africa, and Latin America

2025-02-07

AI Summary

This study investigates the global organizational logic of data labor within AI supply chains, examining how geographic embeddedness and institutional environments shape the stability and flexibility of data work—including data preparation, validation, and simulation—in outsourcing and offshoring practices across France, Madagascar, and Venezuela. Employing multi-sited ethnography and organizational interviews, and integrating theories of relational inequality, institutional embeddedness, and critical global value chain analysis, it offers the first systematic cross-continental comparison of embedded versus market-based data labor supply chain models. Findings reveal that precarity—manifested in low remuneration, insecure employment, and poor working conditions—among data workers in the Global South is not uniform but differentially reproduced according to supply chain architecture. The study uncovers hidden mechanisms of structural exclusion within technical governance and provides empirically grounded insights essential for designing equitable digital policy.

Technology Category

• Philosophy and Ethics of AI: AI & Jobs/Labor
• Humans and AI: Other Foundations of Human Computation & AI
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

This article examines the organisational and geographical forces that shape the supply chains of artificial intelligence (AI) through outsourced and offshored data work. Bridging sociological theories of relational inequalities and embeddedness with critical approaches to Global Value Chains, we conduct a global case study of the digitally enabled organisation of data work in France, Madagascar, and Venezuela. The AI supply chains procure data work via a mix of arm's length contracts through marketplace-like platforms, and of embedded firm-like structures that offer greater stability but less flexibility, with multiple intermediate arrangements. Each solution suits specific types and purposes of data work in AI preparation, verification, and impersonation. While all forms reproduce well-known patterns of exclusion that harm externalised workers especially in the Global South, disadvantage manifests unevenly in different supply chain structures, with repercussions on remunerations, job security and working conditions. Unveiling these processes of contemporary technology development provides insights into possible policy implications.

Problem

• AI supply chain organization
• Global data work disparities
• Impact on workers' conditions

Innovation

• global case study
• relational inequalities and embeddedness
• arm's length contracts and embedded structures