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This platform serves as a living evidence repository for AI governance across the Global South — combining real incident analysis, regulatory interpretation, field research, and practical implementation tools.
Upcoming releases expand this evidence layer with structured evidence packs, practitioner checklists, audit playbooks, and policy briefing material designed for real-world institutional deployment.
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Amazon built an AI to find the best candidates. It ended up filtering out women. Amazon’s hiring tool is a clear example of how gender bias can be embedded and amplified through algorithms. In the Global South, the risks are even higher.
In 2023, a lawsuit revealed how UnitedHealth used an AI system to determine when elderly patients should stop receiving care. The nH Predict case highlights how cost-driven algorithms can override clinical judgment and introduce systemic bias in healthcare decisions. This case raises critical questions for policymakers especially in the Global South about the risks of scaling AI without adequate oversight.
A 2019 case study of the Optum healthcare algorithm showing how proxy bias led to racial disparities and under-served Black patients.
Why unemployment statistics will miss the real disruption of AI in the workforce in the Global South
A Strategic Outlook for Labor, Development, and Artificial Intelligence
Artificial intelligence is beginning to reshape labor markets worldwide, yet most current studies measure its impact using indicators designed for advanced economies. In the Global South, workforce disruption is more likely to appear through rising informality, wage compression, underemployment, and shrinking entry-level opportunities rather than immediate job losses. This policy brief introduces the Global South AI Labor Index and an accompanying AI Labor Risk Dashboard to help governments detect early signals of AI-driven workforce transformation. Together, these tools provide a practical monitoring framework for managing the labor impacts of AI in developing economies.
