--- title: "Statistician: Machine Learning Is Causing A “Crisis in Science”" description: "## Crisis In Science" date: "2019-02-18" modified: "2019-02-18" authors: - name: "Jon Christian" job_title: "Executive Editor" link: "https://futurism.com/authors/jonc" url: "https://futurism.com/neoscope/machine-learning-crisis-science" categories: - "Artificial Intelligence" - "Machine Learning" tags: - "cancer" - "machine learning" - "medicine" - "statistics" --- # Statistician: Machine Learning Is Causing A “Crisis in Science” ![A statistician is warning that scientists are leaning on machine learning algorithms to find patterns in data where none exist.]() *\Image: PHOTON 400 750/VICTOR TANGERMANN\* ## Crisis In Science Rice University statistician Genevera Allen issued a [grave warning]() at a prominent scientific conference this week: that scientists are leaning on machine learning algorithms to find patterns in data even when the algorithms are just fixating on noise that won't be reproduced by another experiment. "There is general recognition of a reproducibility crisis in science right now," she [told the *BBC*](). "I would venture to argue that a huge part of that does come from the use of machine learning techniques in science." ## Reproducibility The problem, according to Allen, can arise when scientists collect a large amount of genome data and then use poorly-understood machine learning algorithms to find clusters of similar genomic profiles. "Often these studies are not found out to be inaccurate until there's another real big dataset that someone applies these techniques to and says 'oh my goodness, the results of these two studies don't overlap,'" she told the *BBC*. ## Noise Project The problem with machine learning, according to Allen, is that it's trained to look for patterns even where none exist. The solution, she suspects, will be in next-generation algorithms that are better able to evaluate how reliable the predictions they make are. "The question is, 'Can we really trust the discoveries that are currently being made using machine-learning techniques applied to large data sets?'" Allen said in a [press release](). "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." **READ MORE:** [AAAS: Machine learning 'causing science crisis']() \[*BBC*\] ***More on machine learning:*** [*Should Coma Patients Live or Die? Machine Learning Will Help Decide.*]() ## Author I'm responsible for editing, assigning, and scouting at Futurism, as well as occasionally writing for the site. That work involves keeping an eye on a wide range of narratives and issues, but over the past few years I've become increasingly interested in how AI is shaping the future of media, the web, and information itself. My day-to-day often involves collaborating on reporting and commentary projects bylined by my colleagues, but I try to find time to do my own reporting as well; stories I've broken for Futurism have been cited by publications including the New York Times, the Washington Post, the New Yorker, Wired, Ars Technica, New York Magazine, the Columbia Journalism Review, Bloomberg, and more. I grew up in Southern Vermont and attended Vanderbilt University. Prior to Futurism, I contributed to outlets including the Boston Globe, Vice, Wired, Slate, the Atlantic, the Outline, Ars Technica and more, and did stints in farming and food service. I currently live in Brooklyn, New York, and in my free time I enjoy pinball, word games, cycling, running, and music production. ### Author social links [Bluesky]()