Joshua Blumenstock, a Minerva-funded researcher had a conversation with government officials from the Togolese Republic in West Africa in regards to how big data machine learning might help them to identify households who have been impacted by COVID-19 in order to provide financial assistance. Big data and artificial intelligence can help in a number of ways including identifying pockets of extreme poverty using deep-learning algorithms trained to process troves of satellite imagery and flag poor regions. Another way is by analyzing mobile-phone data and establishing “no strings attached” mobile cash transfers. The potential of this is enormous but there are also risk associated with using private consumer data and other practical challenges. Blumenstock understands that the stakes of implementing these methods can be high, but understands that a high level of deliberation is not realistic during a crisis. Therefore the new technology-based targeting techniques are designed to be used in tandem with conventional approaches, which rely on government registries and civil society. To succeed these efforts will require better international coordination and learn from the failure and success of others.