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An Anonymous Coward writes:

Machine-learning techniques used by thousands of scientists to analyse data are producing results that are misleading and often completely wrong.

Dr Genevera Allen from Rice University in Houston said that the increased use of such systems was contributing to a "crisis in science".

She warned scientists that if they didn't improve their techniques they would be wasting both time and money. Her research was presented at the American Association for the Advancement of Science in Washington.

A growing amount of scientific research involves using machine learning software to analyse data that has already been collected. This happens across many subject areas ranging from biomedical research to astronomy. The data sets are very large and expensive.

[...] "There is general recognition of a reproducibility crisis in science right now. I would venture to argue that a huge part of that does come from the use of machine learning techniques in science."

The "reproducibility crisis" in science refers to the alarming number of research results that are not repeated when another group of scientists tries the same experiment. It means that the initial results were wrong. One analysis suggested that up to 85% of all biomedical research carried out in the world is wasted effort.

It is a crisis that has been growing for two decades and has come about because experiments are not designed well enough to ensure that the scientists don't fool themselves and see what they want to see in the results.

https://www.bbc.com/news/science-environment-47267081

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