Publication: Transparency and reproducibility in artificial intelligence.
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Date
2020-10-14
Authors
Haibe-Kains, Benjamin
Adam, George Alexandru
Hosny, Ahmed
Khodakarami, Farnoosh
Massive Analysis Quality Control (MAQC) Society Board of Directors
Waldron, Levi
Wang, Bo
McIntosh, Chris
Goldenberg, Anna
Kundaje, Anshul
Advisors
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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
MeSH Terms
Algorithms
Artificial Intelligence
Reproducibility of Results
Artificial Intelligence
Reproducibility of Results