Transparency and reproducibility in artificial intelligence

Benjamin Haibe-Kains*, George Alexandru Adam, Ahmed Hosny, Farnoosh Khodakarami, Thakkar Shraddha, Thakkar Shraddha, Rebecca Kusko, Susanna-Assunta Sansone, Weida Tong, Russ D. Wolfinger, Christopher E. Mason, Wendell Jones, Joaquin Dopazo, Cesare Furlanello, Levi Waldron, Bo Wang, Chris McIntosh, Anna Goldenberg, Anshul Kundaje, Casey S. GreeneTamara Broderick, Michael M. Hoffman, Jeffrey T. Leek, Keegan Korthauer, Wolfgang Huber, Alvis Brazma, Joelle Pineau, Robert Tibshirani, Trevor Hastie, John P. A. Ioannidis, John Quackenbush, Hugo J. W. L. Aerts

*Corresponding author for this work

Research output: Contribution to journalComment/Letter to the editorAcademicpeer-review

440 Downloads (Pure)


Breakthroughs in artificial intelligence (AI) hold enormous potential as it can automate complex tasks and go even beyond human performance. In their study, McKinney et al. showed the high potential of AI for breast cancer screening. However, the lack of methods’ details and algorithm code undermines its scientific value. Here, we identify obstacles hindering transparent and reproducible AI research as faced by McKinney et al., and provide solutions to these obstacles with implications for the broader field.
Original languageEnglish
Pages (from-to)E14-E16
Number of pages7
Issue number7829
Publication statusPublished - 15 Oct 2020


  • AI

Cite this