IDEAS home Printed from https://ideas.repec.org/a/for/ijafaa/y2020i59p32-37.html
   My bibliography  Save this article

Dealing with "Deepfakes": How Synthetic Media Will Distort Reality, Corrupt Data, and Impact Forecasts

Author

Listed:
  • John Wood
  • Nada Sanders

Abstract

Preview and key points from the authors: Distorted data are nothing new. However, deepfake technology-the term is a combination of "deep learning" and "fake"- has created the ability to distort reality in new and alarming ways. This technology is capable of fabricating audio, video, and even text files that are almost indistinguishable from authentic documentation. Machine-learning capabilities are escalating the technology's sophistication, making deepfakes ever more realistic and increasingly resistant to detection. The implications for communication, data integrity, forecasting, and decision making are vast and unequivocally grim. Our best hope for dealing with deepfakes may lie with the creative problem solving of the data-science community, sponsored and supported by corporate leadership. Copyright International Institute of Forecasters, 2020

Suggested Citation

  • John Wood & Nada Sanders, 2020. "Dealing with "Deepfakes": How Synthetic Media Will Distort Reality, Corrupt Data, and Impact Forecasts," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 59, pages 32-37, Fall.
    • Handle: RePEc:for:ijafaa:y:2020:i:59:p:32-37
    as

    Download full text from publisher

    File URL: https://foresight.forecasters.org/shop/
    Download Restriction: no
    ---><---

    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:for:ijafaa:y:2020:i:59:p:32-37. 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: Michael Gilliland (email available below). General contact details of provider: https://edirc.repec.org/data/iiforea.html .

    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.