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The Impact of Transparency in AI Systems on Users’ Data-Sharing Intentions: A Scenario-Based Experiment

In: Artificial Intelligence, Data, and Decision-Making

Author

Listed:
  • Julian Rosenberger

    (Universität Regensburg)

  • Sophie Kuhlemann

    (Friedrich-Alexander-Universität Erlangen-Nürnberg)

  • Verena Tiefenbeck

    (Friedrich-Alexander-Universität Erlangen-Nürnberg)

  • Mathias Kraus

    (Universität Regensburg)

  • Patrick Zschech

    (TU Dresden)

Abstract

Artificial Intelligence (AI) systems are frequently employed in online services to provide personalized experiences to users based on large collections of data. However, AI systems can be designed in different ways, with black-box AI systems appearing as complex data-processing engines and white-box AI systems appearing as fully transparent data-processors. As such, it is reasonable to assume that these different design choices also affect user perception and thus their willingness to share data. To this end, we conducted a pre-registered, scenario-based online experiment with 240 participants and investigated how transparent and non-transparent data-processing entities influenced data-sharing intentions. Surprisingly, our results revealed no significant difference in willingness to share data across entities, challenging the notion that transparency increases data-sharing willingness. Furthermore, we found that a general attitude of trust towards AI has a significant positive influence, especially in the transparent AI condition, whereas privacy concerns did not significantly affect data-sharing decisions.

Suggested Citation

  • Julian Rosenberger & Sophie Kuhlemann & Verena Tiefenbeck & Mathias Kraus & Patrick Zschech, 2026. "The Impact of Transparency in AI Systems on Users’ Data-Sharing Intentions: A Scenario-Based Experiment," Lecture Notes in Information Systems and Organization, in: Christoph M. Flath & Gunther Gust & Frédéric Thiesse & Axel Winkelmann (ed.), Artificial Intelligence, Data, and Decision-Making, pages 477-493, Springer.
    • Handle: RePEc:spr:lnichp:978-3-032-08480-4_30
    DOI: 10.1007/978-3-032-08480-4_30
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