Human Capital

2026-02-03 · Source: AI Now Institute · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Human Resources & Workforce Development, Public Policy & Governance · Depth: Novice, medium

Summary

Joan Kinyua, founding president of the Data Labelers Association of Kenya, argues that the prevailing "human capital" discourse overlooks the critical contributions and exploitation of data workers, predominantly in the Global South. For over a decade, these workers have performed essential tasks like data labeling, content moderation, and transcription, powering AI development. Despite their crucial role, their labor is often dismissed as entry-level or temporary, justifying low pay, lack of social protections, and arbitrary account closures. Kinyua highlights how Big Tech companies exploit this workforce, often with the complicity of Global South governments prioritizing foreign investment over worker rights. She points to Kenya's Business Laws (Amendment) Act, 2024, which shields Big Tech from local lawsuits, as an example of systemic disempowerment. The essay emphasizes the need for transparency, basic rights, and fair compensation for these invisibilized workers.

Key takeaway

For CTOs and VPs of Engineering/Data evaluating AI development strategies, recognize that your models are built on human labor, often under exploitative conditions. Your teams should push for greater transparency in your AI supply chain, ensuring fair pay, basic rights, and social protections for data workers. Prioritize ethical sourcing of data annotation and content moderation to mitigate reputational risks and foster a more sustainable AI ecosystem, rather than relying on systems that invisibilize and disempower essential contributors.

Key insights

Data workers, primarily in the Global South, are exploited and invisibilized despite powering AI development.

Principles

• AI development relies on extensive human data labor.
• "Human capital" discourse often ignores data workers.
• Corporate structures enable accountability evasion.

Method

Workers are pushing back through "name and shame" campaigns and building domestic and global solidarity networks to demand accountability and better treatment where laws fail.

In practice

• Directly name companies in advocacy efforts.
• Form cross-border worker alliances.
• Advocate for worker inclusion in AI development discussions.

Topics

• Data Labeling
• Worker Exploitation
• Global South Labor
• AI Governance
• Tech Worker Solidarity

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Ethicist, Policy Maker, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Now Institute.