Machine learning is contributing to a “reproducibility crisis” within science


Machine learning is contributing to a “reproducibility crisis” within science

Scientific discoveries made using machine learning cannot be automatically trusted, a statistician from Rice University has warned.

A growing trend: Machine-learning systems are increasingly used by scientists across many disciplines to help refine and speed up data analysis. This accelerates their ability to make new discoveries—for example, uncovering new pharmaceutical compounds.

The problem? Genevera Allen, associate professor at Rice University, has warned that the adoption of machine learning techniques is contributing to a growing “reproducibility crisis” in science, where a worrying number of research findings cannot be repeated by other researchers, thus casting doubt on the validity of the initial results. “I would venture to argue that a huge part of that does come from the use of machine-learning techniques in science,” Allen told the BBC. In many situations, discoveries made this way shouldn’t be trusted until they have been checked, she argued.

On the plus side: There is work under way on the next generation of machine-learning systems to make sure they’re able to assess the uncertainty and reproducibility of their predictions, Allen said.


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