There is a growing chorus of warnings that China's economy will slow in 2025. One major driving factor of last year's growth is now at risk: exports. China has relied on manufacturing to help exit the slowdown - so, it has been exporting a record number of electric vehicles, 3D printers and industrial robots. The US, Canada and the European Union have accused China of making too many goods and imposed tariffs on Chinese imports to protect domestic jobs and businesses. Experts say Chinese exporters may now focus on other parts of the world.

But in the biggest ever study of real-world mortgage data, economists Laura Blattner at Stanford University and Scott Nelson at the University of Chicago show that differences in mortgage approval between minority and majority groups is not just down to bias, but to the fact that minority and low-income groups have less data in their credit histories. This means that when this data is used to calculate a credit score and this credit score used to make a prediction on loan default, then that prediction will be less precise. It is this lack of precision that leads to inequality, not just bias. The implications are stark: fairer algorithms won't fix the problem. "It's a really striking result," says Ashesh Rambachan, who studies machine learning and economics at Harvard University, but was not involved in the study.