Can we trust scientific discoveries made using machine learning?

#artificialintelligence

Rice University statistician Genevera Allen says scientists must keep questioning the accuracy and reproducibility of scientific discoveries made by machine-learning techniques until researchers develop new computational systems that can critique themselves. 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.


Machine learning 'causing science crisis'

#artificialintelligence

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.


Machine learning 'causing science crisis'

#artificialintelligence

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.


Can we trust scientific discoveries made using machine learning?

#artificialintelligence

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.


Rice statistician's warning grabs headlines around the globe Statistics

#artificialintelligence

Rice University statistician Genevera Allen knew she was raising an important issue when she spoke earlier this month at the American Association for the Advancement of Science (AAAS) annual meeting in Washington, but she was surprised by the magnitude of the response. Allen, associate professor of statistics and founding director of Rice's Center for Transforming Data to Knowledge (D2K Lab), used the forum to raise awareness about the potential lack of reproducibility of data-driven discoveries produced by machine learning (ML). She cautioned her audience not to assume that today's scientific discoveries made via ML are accurate or reproducible. She said that many commonly used ML techniques are designed to always make a prediction and are not designed to report on the uncertainty of the finding. Her comments garnered worldwide media attention, with some commentators questioning the value of ML in data science.