Humanitarian aid guided by satellite data may harm marginalised groups

Satellite data can help policy-makers quickly identify areas of the world in need of aid and development, but research shows it can also contain bias against marginalised groups, potentially compromising policy goals. Machine-learning systems that scan satellite images for indicators of poverty or disaster damage are becoming a popular tool for assessing humanitarian and development needs. But Lukas Kondmann and Xiao Xiang Zhu at the German Aerospace Center in Cologne say little attention is being paid to potential biases built into this data.