IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v586y2020i7829d10.1038_s41586-020-2766-y.html
   My bibliography  Save this article

Transparency and reproducibility in artificial intelligence

Author

Listed:
  • Benjamin Haibe-Kains

    (University Health Network
    University of Toronto
    University of Toronto
    Ontario Institute for Cancer Research)

  • George Alexandru Adam

    (University of Toronto
    Vector Institute for Artificial Intelligence)

  • Ahmed Hosny

    (Brigham and Women’s Hospital, Harvard Medical School
    Brigham and Women’s Hospital, Harvard Medical School)

  • Farnoosh Khodakarami

    (University Health Network
    University of Toronto)

  • Levi Waldron

    (CUNY Graduate School of Public Health and Health Policy)

  • Bo Wang

    (University of Toronto
    University of Toronto
    Vector Institute for Artificial Intelligence
    University Health Network)

  • Chris McIntosh

    (University of Toronto
    Vector Institute for Artificial Intelligence
    University Health Network)

  • Anna Goldenberg

    (University of Toronto
    Vector Institute for Artificial Intelligence
    SickKids Research Institute
    CIFAR)

  • Anshul Kundaje

    (Stanford University School of Medicine
    Stanford University)

  • Casey S. Greene

    (University of Pennsylvania
    Alex’s Lemonade Stand Foundation)

  • Tamara Broderick

    (Massachusetts Institute of Technology)

  • Michael M. Hoffman

    (University Health Network
    University of Toronto
    University of Toronto
    Vector Institute for Artificial Intelligence)

  • Jeffrey T. Leek

    (Johns Hopkins Bloomberg School of Public Health)

  • Keegan Korthauer

    (University of British Columbia
    BC Children’s Hospital Research Institute)

  • Wolfgang Huber

    (Genome Biology Unit)

  • Alvis Brazma

    (European Bioinformatics Institute, EMBL-EBI)

  • Joelle Pineau

    (McGill University
    Montreal Institute for Learning Algorithms)

  • Robert Tibshirani

    (Stanford University School of Humanities and Sciences
    Stanford University School of Medicine)

  • Trevor Hastie

    (Stanford University School of Humanities and Sciences
    Stanford University School of Medicine)

  • John P. A. Ioannidis

    (Stanford University School of Humanities and Sciences
    Stanford University School of Medicine
    Stanford University School of Medicine
    Meta-Research Innovation Center at Stanford (METRICS))

  • John Quackenbush

    (Harvard T.H Chan School of Public Health
    Brigham and Women’s Hospital
    Dana-Farber Cancer Institute)

  • Hugo J. W. L. Aerts

    (Brigham and Women’s Hospital, Harvard Medical School
    Brigham and Women’s Hospital, Harvard Medical School
    Maastricht University
    Massachusetts General Hospital, Harvard Medical School)

Abstract

No abstract is available for this item.

Suggested Citation

  • Benjamin Haibe-Kains & George Alexandru Adam & Ahmed Hosny & Farnoosh Khodakarami & Levi Waldron & Bo Wang & Chris McIntosh & Anna Goldenberg & Anshul Kundaje & Casey S. Greene & Tamara Broderick & Mi, 2020. "Transparency and reproducibility in artificial intelligence," Nature, Nature, vol. 586(7829), pages 14-16, October.
  • Handle: RePEc:nat:nature:v:586:y:2020:i:7829:d:10.1038_s41586-020-2766-y
    DOI: 10.1038/s41586-020-2766-y
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41586-020-2766-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1038/s41586-020-2766-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rostami-Tabar, Bahman & Ali, Mohammad M. & Hong, Tao & Hyndman, Rob J. & Porter, Michael D. & Syntetos, Aris, 2022. "Forecasting for social good," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1245-1257.
    2. Igna, Ioana & Venturini, Francesco, 2023. "The determinants of AI innovation across European firms," Research Policy, Elsevier, vol. 52(2).
    3. Bi, Xuan & Shen, Xiaotong, 2023. "Distribution-invariant differential privacy," Journal of Econometrics, Elsevier, vol. 235(2), pages 444-453.
    4. Mohd Nasrullah Nik Ab Kadir & Najib Majdi Yaacob & Siti Norbayah Yusof & Imi Sairi Ab Hadi & Kamarul Imran Musa & Seoparjoo Azmel Mohd Isa & Balqis Bahtiar & Farzaana Adam & Maya Mazuwin Yahya & Suhai, 2022. "Development of Predictive Models for Survival among Women with Breast Cancer in Malaysia," IJERPH, MDPI, vol. 19(22), pages 1-14, November.
    5. Plantinga, Paul, 2022. "Digital discretion and public administration in Africa: Implications for the use of artificial intelligence," SocArXiv 2r98w, Center for Open Science.
    6. Juan Carlos Henao & Liliana López-Jiménez, 2021. "Disrupción tecnológica, transformación digital y sociedad. Tomo IV, Aires de revolución : nuevos desafíos tecnológicos a las instituciones económicas, financieras y organizacionales de nuestros tiempo," Books, Universidad Externado de Colombia, Facultad de Derecho, number 1283.
    7. Mathieu Chevrier & Brice Corgnet & Eric Guerci & Julie Rosaz, 2024. "Algorithm Credulity: Human and Algorithmic Advice in Prediction Experiments," GREDEG Working Papers 2024-03, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    8. Chen, Zhongfei & Jiang, Kangqi, 2024. "Digitalization and corporate investment efficiency: Evidence from China," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:nature:v:586:y:2020:i:7829:d:10.1038_s41586-020-2766-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.