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In the AI science boom, beware: your results are only as good as your data Machine-learning systems are voracious data consumers - but trustworthy results require more vetting both before and after publication.

February 1, 2024

Nature

Hunter Moseley

"We are in the middle of a data-driven science boom. Huge, complex data sets, often with large numbers of individually measured and annotated ‘features’, are fodder for voracious artificial intelligence (AI) and machine-learning systems, with details of new applications being published almost daily.

But publication in itself is not synonymous with factuality. Just because a paper, method or data set is published does not mean that it is correct and free from mistakes. Without checking for accuracy and validity before using these resources, scientists will surely encounter errors. In fact, they already have."

A flurry of research misconduct cases has universities scrambling to protect themselves

February 12, 2024

STAT

Angus Chen and Jonathan Wosen

"There was a time when an allegation of data mishandling, scientific misconduct, or just a technical error felt like a crisis to Barrett Rollins, an oncologist and research integrity officer at Dana-Farber Cancer Institute. Now, it’s just another Tuesday.

The renowned cancer treatment and research center is in the midst of a lengthy review of possible discrepancies involving around 60 papers co-authored by four of its top researchers over a period of over 15 years, including CEO Laurie Glimcher and COO William Hahn. And it’s hardly alone. Over the past decade, the number of research misconduct allegations reported to the National Institutes of Health has more than doubled, climbing from 74 in 2013 to 169 in 2022. And scientific sleuths are finding plenty of other problems that don’t always qualify as outright misconduct."