The importance of transparency and reproducibility in artificial intelligence research

28 Feb 2020  ·  Benjamin Haibe-Kains, George Alexandru Adam, Ahmed Hosny, Farnoosh Khodakarami, MAQC Society Board, Levi Waldron, Bo Wang, Chris McIntosh, Anshul Kundaje, Casey S. Greene, Michael M. Hoffman, Jeffrey T. Leek, Wolfgang Huber, Alvis Brazma, Joelle Pineau, Robert Tibshirani, Trevor Hastie, John P. A. Ioannidis, John Quackenbush, Hugo J. W. L. Aerts ·

In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening. However, the lack of detailed methods and computer code undermines its scientific value. We identify obstacles hindering transparent and reproducible AI research as faced by McKinney et al and provide solutions with implications for the broader field.

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