IDEAS home Printed from https://ideas.repec.org/a/nat/nathum/v7y2023i11d10.1038_s41562-023-01734-2.html
   My bibliography  Save this article

Psychological factors underlying attitudes toward AI tools

Author

Listed:
  • Julian Freitas

    (Harvard Business School)

  • Stuti Agarwal

    (Harvard Business School)

  • Bernd Schmitt

    (Columbia Business School)

  • Nick Haslam

    (University of Melbourne)

Abstract

What are the psychological factors driving attitudes toward artificial intelligence (AI) tools, and how can resistance to AI systems be overcome when they are beneficial? Here we first organize the main sources of resistance into five main categories: opacity, emotionlessness, rigidity, autonomy and group membership. We relate each of these barriers to fundamental aspects of cognition, then cover empirical studies providing correlational or causal evidence for how the barrier influences attitudes toward AI tools. Second, we separate each of the five barriers into AI-related and user-related factors, which is of practical relevance in developing interventions towards the adoption of beneficial AI tools. Third, we highlight potential risks arising from these well-intentioned interventions. Fourth, we explain how the current Perspective applies to various stakeholders, including how to approach interventions that carry known risks, and point to outstanding questions for future work.

Suggested Citation

  • Julian Freitas & Stuti Agarwal & Bernd Schmitt & Nick Haslam, 2023. "Psychological factors underlying attitudes toward AI tools," Nature Human Behaviour, Nature, vol. 7(11), pages 1845-1854, November.
    • Handle: RePEc:nat:nathum:v:7:y:2023:i:11:d:10.1038_s41562-023-01734-2
    DOI: 10.1038/s41562-023-01734-2
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41562-023-01734-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1038/s41562-023-01734-2?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. Mourelatos, Evangelos & Zervas, Panagiotis & Lagios, Dimitris & Tzimas, Giannis, 2024. "Can AI Bridge the Gender Gap in Competitiveness?," GLO Discussion Paper Series 1404, Global Labor Organization (GLO).

    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:nathum:v:7:y:2023:i:11:d:10.1038_s41562-023-01734-2. 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.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.