Explore our latest articles, tutorials, and insights
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.
A case study examining the opportunities, risks, regulations, and ecosystem of AI in healthcare in India.
Anthropic’s recent study on AI and jobs offers valuable insights into how artificial intelligence may affect labor markets in advanced economies. But the same framework does not fully capture how workforce disruption unfolds in the Global South—where informality, youth employment pressures, and service outsourcing shape labor market realities.
The NIST AI Risk Management Framework (AI RMF) is increasingly treated as a global blueprint for “trustworthy AI.” But what happens when a framework designed for resource-rich Western institutions is applied to the Global South?
Why the EU AI Act—designed for data-rich, institutionally mature European economies—breaks down when applied to the Global South.
As AI enters courts and welfare systems worldwide, the COMPAS debate reveals a critical lesson: fairness depends on context, and exporting models without reform risks scaling inequality.