IDEAS home Printed from https://ideas.repec.org/a/spr/binfse/v63y2021i2d10.1007_s12599-021-00683-2.html
   My bibliography  Save this article

Expl(AI)n It to Me – Explainable AI and Information Systems Research

Author

Listed:
  • Kevin Bauer

    (Leibniz Institute for Financial Research SAFE)

  • Oliver Hinz

    (Goethe University Frankfurt)

  • Wil Aalst

    (RWTH Aachen)

  • Christof Weinhardt

    (Karlsruhe Institute of Technology (KIT))

Abstract

No abstract is available for this item.

Suggested Citation

  • Kevin Bauer & Oliver Hinz & Wil Aalst & Christof Weinhardt, 2021. "Expl(AI)n It to Me – Explainable AI and Information Systems Research," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(2), pages 79-82, April.
    • Handle: RePEc:spr:binfse:v:63:y:2021:i:2:d:10.1007_s12599-021-00683-2
    DOI: 10.1007/s12599-021-00683-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12599-021-00683-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12599-021-00683-2?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. Julia Brasse & Hanna Rebecca Broder & Maximilian Förster & Mathias Klier & Irina Sigler, 2023. "Explainable artificial intelligence in information systems: A review of the status quo and future research directions," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-30, December.
    2. Greiner, Ben & Grünwald, Philipp & Lindner, Thomas & Lintner, Georg & Wiernsperger, Martin, 2024. "Incentives, Framing, and Reliance on Algorithmic Advice: An Experimental Study," Department for Strategy and Innovation Working Paper Series 01/2024, WU Vienna University of Economics and Business.
    3. Kevin Bauer & Moritz von Zahn & Oliver Hinz, 2023. "Expl(AI)ned: The Impact of Explainable Artificial Intelligence on Users’ Information Processing," Information Systems Research, INFORMS, vol. 34(4), pages 1582-1602, December.

    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:spr:binfse:v:63:y:2021:i:2:d:10.1007_s12599-021-00683-2. 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.springer.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.