Could machine learning fuel a reproducibility crisis in science?

A CT scan of a tumor in human lungs. Researchers are experimenting with AI algorithms that can spot early signs of the disease.Credit: K. H. Fung/SPL From biomedicine to political sciences, researchers increasingly use machine learning as a tool to make predictions on the basis of patterns in their data. But the claims in many such studies are likely to be overblown, according to a pair of researchers at Princeton University in New Jersey. They want to sound an alarm about what they call a "brewing reproducibility crisis" in machine-learning-based sciences. Machine learning is being sold as a tool that researchers can learn in a few hours and use by themselves -- and many follow that advice, says Sayash Kapoor, a machine-learning researcher at Princeton.