학술논문
The importance of transparency and reproducibility in artificial intelligence research
Document Type
Working Paper
Author
Haibe-Kains, Benjamin; Adam, George Alexandru; Hosny, Ahmed; Khodakarami, Farnoosh; Board, MAQC Society; Waldron, Levi; Wang, Bo; McIntosh, Chris; Kundaje, Anshul; Greene, Casey S.; Hoffman, Michael M.; Leek, Jeffrey T.; Huber, Wolfgang; Brazma, Alvis; Pineau, Joelle; Tibshirani, Robert; Hastie, Trevor; Ioannidis, John P. A.; Quackenbush, John; Aerts, Hugo J. W. L.
Source
Nature 586 (2020) E14-E16
Subject
Language
Abstract
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.