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https://dx.doi.org/10.48550/ar...
Article . 2020
License: CC BY NC SA
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The importance of transparency and reproducibility in artificial intelligence research

Authors: Haibe-Kains, Benjamin; Adam, George Alexandru; Hosny, Ahmed; Khodakarami, Farnoosh; Board, MAQC Society; Waldron, Levi; Wang, Bo; +13 Authors

The importance of transparency and reproducibility in artificial intelligence research

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.

Keywords

FOS: Computer and information sciences, Applications (stat.AP), Statistics - Applications

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Related to Research communities
Cancer Research