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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