Ghost Workers of Nairobi: Geographic Pay Disparities and Kenya’s AI Data-Labour Economy

Sean Morris, Robert D. Austin

Product #:W52314

Supplier:Ivey

Discipline:General Management, International Business

Setting:Kenya, United States, 2026

Details:Case (PDF) • 9 pages • 2026-10-08

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This case and accompanying exercise explore managerial and ethical questions about the hidden human labour behind artificial intelligence (AI) development. The training of generative AI products has depended on large workforces performing repetitive, often emotionally taxing work, much of it outsourced to workers in the Global South, where wages are low, legal protections are limited, and the companies benefiting are far removed. This exercise asks students the consider whether and how they might partner with a Nairobi-based outsourcing firm. It invites them to consider whether cost savings built on geographic pay disparities can be ethically justified, and it raises questions about corporate accountability and what it means to build technology responsibly.

Learning Objectives

The case and the supplemental exercise were developed for undergraduate, graduate, and executive courses on business ethics, responsible AI and technology policy, supply-chain management, corporate social responsibility and environmental, social, and governance frameworks, and international business. The pairing is suitable as a centrepiece discussion in any course addressing the intersection of commercial decision-making and ethical obligation. The materials work best when students have been introduced to basic stakeholder theory and at least one ethical framework (utilitarian, deontological, or structural). They can function together as a capstone project that integrates those frameworks under commercial pressure or as an earlier case that uses a concrete decision to motivate the frameworks before they are formally introduced.

After working through the case and related materials, students will be able to do the following:

Analyze a multi-dimensional sourcing decision that requires trading off cost, reputation, regulatory exposure, and ethical obligation, and defend a specific recommendation to a divided board.
Evaluate the ethical legitimacy of structuring a cost advantage around the constrained labour market of a lower-wage jurisdiction, including the conditions under which cost-of-living alignment tips into wage stratification.
Compare contractual, market-substitution, vertical-integration, regulatory, and civil-society mechanisms for governing labour conditions in global AI supply chains, including their relative strengths, limitations, and risks.
Critically assess whether “responsible-AI” positioning is coherent when supply-chain labour practices are excluded from its scope, and identify the organizational consequences of value statements that do not survive commercial pressure.
Working from a documented, contested public record, reconcile competing factual claims and reason responsibly under genuine evidentiary uncertainty.

Ghost Workers of Nairobi: Geographic Pay Disparities and Kenya’s AI Data-Labour Economy

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Learning Objectives