Machine learning systems and the use of big data sets has accelerated the reproducibility crisis in science, Genevera Allen says. Machine-learning techniques used by thousands of scientists to analyze data are contributing to the reproducibility crisis in science by producing results that are misleading and often wrong. Genevera Allen of Rice University warns scientists that if they don't improve their techniques they will be wasting both time and money. A growing amount of scientific research involves using machine learning software to analyze data that has already been collected. Allen says the answers they come up with are likely to be inaccurate or wrong because the software is identifying patterns that exist only in that data set and not the real world.

Allen, associate professor of statistics, computer science and electrical and computer engineering at Rice and of pediatrics-neurology at Baylor College of Medicine, will address the topic in both a press briefing and a general session today at the 2019 Annual Meeting of the American Association for the Advancement of Science (AAAS). "The question is, 'Can we really trust the discoveries that are currently being made using machine-learning techniques applied to large data sets?'" "The answer in many situations is probably, 'Not without checking,' but work is underway on next-generation machine-learning systems that will assess the uncertainty and reproducibility of their predictions." Machine learning (ML) is a branch of statistics and computer science concerned with building computational systems that learn from data rather than following explicit instructions. Allen said much attention in the ML field has focused on developing predictive models that allow ML to make predictions about future data based on its understanding of data it has studied. "A lot of these techniques are designed to always make a prediction," she said.