IDEAS home Printed from https://ideas.repec.org/a/wly/fufsci/v3y2021i2ne89.html
   My bibliography  Save this article

Beware of Bureaucrats: A commentary on Lustick and Tetlock (2021)

Author

Listed:
  • Heiko A. von der Gracht

Abstract

No abstract is available for this item.

Suggested Citation

  • Heiko A. von der Gracht, 2021. "Beware of Bureaucrats: A commentary on Lustick and Tetlock (2021)," Futures & Foresight Science, John Wiley & Sons, vol. 3(2), June.
    • Handle: RePEc:wly:fufsci:v:3:y:2021:i:2:n:e89
    DOI: 10.1002/ffo2.89
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/ffo2.89
    Download Restriction: no
    File URL: https://libkey.io/10.1002/ffo2.89?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
    ---><---

    References listed on IDEAS

    as
    1. Arun Rai, 2020. "Explainable AI: from black box to glass box," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 137-141, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Changdong, 2024. "How consumers respond to service failures caused by algorithmic mistakes: The role of algorithmic interpretability," Journal of Business Research, Elsevier, vol. 176(C).
    2. Leah Warfield Smith & Randall Lee Rose & Alex R. Zablah & Heath McCullough & Mohammad “Mike” Saljoughian, 2023. "Examining post-purchase consumer responses to product automation," Journal of the Academy of Marketing Science, Springer, vol. 51(3), pages 530-550, May.
    3. Ekaterina Jussupow & Kai Spohrer & Armin Heinzl & Joshua Gawlitza, 2021. "Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision-Making Process with Artificial Intelligence," Information Systems Research, INFORMS, vol. 32(3), pages 713-735, September.
    4. Trocin, Cristina & Hovland, Ingrid Våge & Mikalef, Patrick & Dremel, Christian, 2021. "How Artificial Intelligence affords digital innovation: A cross-case analysis of Scandinavian companies," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    5. Mari, Alex & Mandelli, Andreina & Algesheimer, René, 2024. "Empathic voice assistants: Enhancing consumer responses in voice commerce," Journal of Business Research, Elsevier, vol. 175(C).
    6. Vinay Singh & Brijesh Nanavati & Arpan Kumar Kar & Agam Gupta, 2023. "How to Maximize Clicks for Display Advertisement in Digital Marketing? A Reinforcement Learning Approach," Information Systems Frontiers, Springer, vol. 25(4), pages 1621-1638, August.
    7. Alex Mari & Andreina Mandelli & René Algesheimer, 2023. "Shopping with Voice Assistants: How Empathy Affects Individual and Family Decision-Making Outcomes," Working Papers 399, University of Zurich, Department of Business Administration (IBW).
    8. Valeria D’Amato & Rita D’Ecclesia & Susanna Levantesi, 2022. "ESG score prediction through random forest algorithm," Computational Management Science, Springer, vol. 19(2), pages 347-373, June.
    9. Patrick Rehill & Nicholas Biddle, 2023. "Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability," Papers 2310.13240, arXiv.org, revised Mar 2024.
    10. Piotr Tomasz Makowski & Yuya Kajikawa, 2021. "Automation-driven innovation management? Toward Innovation-Automation-Strategy cycle," Papers 2103.02395, arXiv.org.
    11. Janasz, Tomasz & Mortensen, Peter & Reisswig, Christian & Weller, Tobias & Herrmann, Maximilian & Crnoja, Ivona & Höhne, Johannes, 2021. "Advancements in ML-Enabled Intelligent Document Processing and How to Overcome Adoption Challenges in Enterprises," Die Unternehmung - Swiss Journal of Business Research and Practice, Nomos Verlagsgesellschaft mbH & Co. KG, vol. 75(3), pages 340-358.
    12. Ho, Shirley S. & Cheung, Justin C., 2024. "Trust in artificial intelligence, trust in engineers, and news media: Factors shaping public perceptions of autonomous drones through UTAUT2," Technology in Society, Elsevier, vol. 77(C).
    13. Abdallah Moubayed & Abdallah Shami & Anwer Al-Dulaimi, 2022. "On End-to-End Intelligent Automation of 6G Networks," Future Internet, MDPI, vol. 14(6), pages 1-28, May.
    14. Aimé, Isabelle & Berger-Remy, Fabienne & Laporte, Marie-Eve, 2022. "The brand, the persona and the algorithm: How datafication is reconfiguring marketing work☆," Journal of Business Research, Elsevier, vol. 145(C), pages 814-827.
    15. John-Mathews, Jean-Marie, 2022. "Some critical and ethical perspectives on the empirical turn of AI interpretability," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    16. Scott Thiebes & Sebastian Lins & Ali Sunyaev, 2021. "Trustworthy artificial intelligence," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 447-464, June.
    17. Ming-Hui Huang & Roland T. Rust, 2021. "A strategic framework for artificial intelligence in marketing," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 30-50, January.
    18. Söderlund, Magnus & Natorina, Alona, 2024. "Service robots in a multi-party setting: An examination of robots’ ability to detect human-to-human conflict and its effects on robot evaluations," Technology in Society, Elsevier, vol. 77(C).
    19. Akter, Shahriar & Hossain, Md Afnan & Sajib, Shahriar & Sultana, Saida & Rahman, Mahfuzur & Vrontis, Demetris & McCarthy, Grace, 2023. "A framework for AI-powered service innovation capability: Review and agenda for future research," Technovation, Elsevier, vol. 125(C).
    20. Alantari, Huwail J. & Currim, Imran S. & Deng, Yiting & Singh, Sameer, 2022. "An empirical comparison of machine learning methods for text-based sentiment analysis of online consumer reviews," International Journal of Research in Marketing, Elsevier, vol. 39(1), pages 1-19.

    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:wly:fufsci:v:3:y:2021:i:2:n:e89. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)2573-5152 .

    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.