This viewpoint discusses how big data and artificial intelligence can help coordinate aid relief in developing countries where governments may lack data on household incomes.

The author presents theoretical, planned, and executed scenarios including:

• Processing satellite imagery with deep learning algorithms to identify pockets of extreme poverty
• Using mobile phone data to help direct government assistance to subscribers who live in certain vulnerable communities
• Identifying patterns in phone logs to determine whether individual subscribers meet more nuanced eligibility criteria for emergency assistance, such as being below the poverty line or most affected by stay-at-home orders

The author describes the enormous potential of these methods to dramatically increase the timeliness and effectiveness of humanitarian responses — while minimizing the need for face-to-face contact with government employees in the middle of a pandemic. He also discusses ethical and practical challenges, including the risks associated with using private consumer data.