Publication: Transparency and Reproducibility in Artificial Intelligence
Loading...
Open/View Files
Date
2020-10-14
Published Version
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Science and Business Media LLC
The Harvard community has made this article openly available. Please share how this access benefits you.
Citation
Haibe-Kains, Benjamin, George Alexandru Adam, Ahmed Hosny, Farnoosh Khodakarami, Levi Waldron, Bo Wang, Chris McIntosh et al. "Transparency and Reproducibility in Artificial Intelligence." Nature 586, no. 7829 (2020): E14-E16. DOI: 10.1038/s41586-020-2766-y
Abstract
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.
Description
Other Available Sources
Research Data
Keywords
Research Subject Categories::MEDICINE::Physiology and pharmacology::Physiology::Medical technology
Terms of Use
This article is made available under the terms and conditions applicable to Other Posted Material (LAA), as set forth at Terms of Service