IDEAS home Printed from https://ideas.repec.org/h/spr/lnichp/978-3-030-86797-3_16.html
   My bibliography  Save this book chapter

The Evaluation of the Black Box Problem for AI-Based Recommendations: An Interview-Based Study

In: Innovation Through Information Systems

Author

Listed:
  • Jessica Ochmann

    (Friedrich-Alexander-University Erlangen-Nuremberg)

  • Sandra Zilker

    (Friedrich-Alexander-University Erlangen-Nuremberg)

  • Sven Laumer

    (Friedrich-Alexander-University Erlangen-Nuremberg)

Abstract

Organizations are increasingly adopting artificial intelligence (AI) for business processes. AI-based recommendations aim at supporting users in decision-making, e.g., by pre-filtering options. However, users can often hardly understand how these recommendations are developed. This issue is called “black box problem”. In the context of Human Resources Management, this leads to new questions regarding the acceptance of AI-based recommendations in the recruiting process. Therefore, we develop a model based on the theory of planned behavior explaining the relation between the user’s perception of the black box problem and the attitude toward AI-based recommendations distinguishing between a mandatory and voluntary use context. We conducted 21 interviews with experts from recruiting and AI. Our results show that the perception of the black box problem conceptualized by the awareness and the evaluated relevance relates to the user’s attitude toward AI-based recommendations. Further, we show that the use context has a moderating effect on that relation.

Suggested Citation

  • Jessica Ochmann & Sandra Zilker & Sven Laumer, 2021. "The Evaluation of the Black Box Problem for AI-Based Recommendations: An Interview-Based Study," Lecture Notes in Information Systems and Organization, in: Frederik Ahlemann & Reinhard Schütte & Stefan Stieglitz (ed.), Innovation Through Information Systems, pages 232-246, Springer.
    • Handle: RePEc:spr:lnichp:978-3-030-86797-3_16
    DOI: 10.1007/978-3-030-86797-3_16
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

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


    Cited by:

    1. Alain Lacroux & Christelle Martin Lacroux, 2022. "Believing Or Not In Algorithms... ? Recruiters' Perceptions And Behavior Towards Algorithms During Resume Screening [Croire Ou Ne Pas Croire Les Algorithmes… ? Perceptions Et Comportement Des Recru," Post-Print hal-04095500, HAL.

    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:lnichp:978-3-030-86797-3_16. 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.