Corpus ID: 211678428

The importance of transparency and reproducibility in artificial intelligence research

@article{HaibeKains2020TheIO,
  title={The importance of transparency and reproducibility in artificial intelligence research},
  author={B. Haibe-Kains and George Adam and A. Hosny and F. Khodakarami and Maqc Society Board and L. Waldron and B. Wang and C. McIntosh and A. Kundaje and C. Greene and M. M. Hoffman and J. Leek and W. Huber and A. Brazma and Joelle Pineau and R. Tibshirani and T. Hastie and J. Ioannidis and John Quackenbush and H. Aerts},
  journal={arXiv: Applications},
  year={2020}
}
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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