The importance of transparency and reproducibility in artificial intelligence research

Massive Analysis Quality Control (MAQC) Society Board of Directors

The importance of transparency and reproducibility in artificial intelligence research

Massive Analysis Quality Control (MAQC) Society Board of Directors

Bloomberg School of Public Health

Research output: Contribution to journal › Article › peer-review

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.

Original language	English (US)
Journal	Unknown Journal
State	Published - Feb 28 2020

ASJC Scopus subject areas

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title = "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.",

author = "{Massive Analysis Quality Control (MAQC) Society Board of Directors} and Benjamin Haibe-Kains and Adam, {George Alexandru} and Ahmed Hosny and Farnoosh Khodakarami and Levi Waldron and Bo Wang and Chris McIntosh and Anshul Kundaje and Greene, {Casey S.} and Hoffman, {Michael M.} and Leek, {Jeffrey T.} and Wolfgang Huber and Alvis Brazma and Joelle Pineau and Robert Tibshirani and Trevor Hastie and Ioannidis, {John P.A.} and John Quackenbush and Aerts, {Hugo J.W.L.} and Thakkar Shraddha and Rebecca Kusko and Sansone, {Susanna Assunta} and Weida Tong and Wolfinger, {Russ D.} and Christopher Mason and Wendell Jones and Joaquin Dopazo and Cesare Furlanello",

year = "2020",

month = feb,

day = "28",

language = "English (US)",

journal = "Advances in Water Resources",

issn = "0309-1708",

publisher = "Elsevier Limited",

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TY - JOUR

T1 - The importance of transparency and reproducibility in artificial intelligence research

AU - Massive Analysis Quality Control (MAQC) Society Board of Directors

AU - Haibe-Kains, Benjamin

AU - Adam, George Alexandru

AU - Hosny, Ahmed

AU - Khodakarami, Farnoosh

AU - Waldron, Levi

AU - Wang, Bo

AU - McIntosh, Chris

AU - Kundaje, Anshul

AU - Greene, Casey S.

AU - Hoffman, Michael M.

AU - Leek, Jeffrey T.

AU - Huber, Wolfgang

AU - Brazma, Alvis

AU - Pineau, Joelle

AU - Tibshirani, Robert

AU - Hastie, Trevor

AU - Ioannidis, John P.A.

AU - Quackenbush, John

AU - Aerts, Hugo J.W.L.

AU - Shraddha, Thakkar

AU - Kusko, Rebecca

AU - Sansone, Susanna Assunta

AU - Tong, Weida

AU - Wolfinger, Russ D.

AU - Mason, Christopher

AU - Jones, Wendell

AU - Dopazo, Joaquin

AU - Furlanello, Cesare

PY - 2020/2/28

Y1 - 2020/2/28

N2 - 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.

AB - 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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M3 - Article

AN - SCOPUS:85095628689

JO - Advances in Water Resources

JF - Advances in Water Resources

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